Competitiveness of Battery Energy Storage in the Future ...

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Competitiveness of Battery Energy

Storage in the Future Belgian Capacity

Market

Master thesis submitted to Delft University of Technology

in partial fulfilment of the requirements for the degree of

MASTER OF SCIENCE

in

Engineering and Policy Analysis

Faculty of Technology, Policy and Management

by

Theodorus Johannes Albertus (Duco) Slooff

Student number: 4295749

To be defended in public on November 26, 2019

Graduation committee

Chair and first supervisor: Dr.ir. L.J. de Vries, Energy and Industry

Second supervisor: Dr. M.E. Warnier, Systems Engineering

External Supervisor: Ir. J.J. Hettema, Eneco

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Acknowledgements Throughout the writing of this master thesis, I have received support during various stages. I

express my sincere thanks to my academic supervisors Laurens de Vries and Martijn Warnier, for

their constructive feedback. I would also like to thank my external supervisor Jesse Hettema for

his guidance during my internship at Eneco. Furthermore, Jeroen and Paul from Eneco for the

discussions that have led to interesting insights.

Duco Slooff

Delft, 2019

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Executive Summary

As the world is transitioning from conventional to renewable energy sources,

governments and energy utilities face the challenge to match the supply profile of these

intermittent sources to the energy demand. Energy storage is being considered as one of

the potential solutions to cope with the variability of renewables.

Belgium is not only challenged by the transition to renewable energy but must also deal

with the total nuclear power phase-out by 2025. The country significantly depends on the

seven nuclear power plants since they account for more than half of the national

electricity production (depending on the availability of the reactors). The Belgian TSO

Elia has investigated the urgency of adequacy and flexible electricity sources and

proposed a Capacity Remuneration Mechanism (CRM), which should encourage

investments in generation capacity to maintain the security of supply in Belgium. All

awarded capacity providers of the competitive auction receive financial compensation

for the availability of their generation capacity in the Belgian energy market.

Eneco is one of the largest utility companies in the Belgian electricity market for

consumers and provides clean energy through their wind and solar farms. However,

Eneco has no flexible generation capacity in Belgium, which makes the company more

sensitive to high electricity prices because of the abundant supply of solar and wind

energy. Electrical Energy Storage (EES) can reduce the risk of financial losses during high

electricity prices and can be seen as an additional income-generating asset next to

Eneco’s existing wind and solar assets in Belgium. Therefore, Eneco considers

participating in the Belgian capacity auction with EES capacity. As a result, this leads to

the following main research question:

What is the competitiveness of electrical energy storage in the future Belgian capacity

market?

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The competitiveness of EES is assessed based on the constructed competitiveness

evaluation model. The model considers the long-term capacity remuneration, possible

costs related to the capacity market, storage costs, and revenue derived in the electricity

market. A capacity provider is considered as competitive if the expected costs are smaller

than the expected revenue from being in the capacity market. The net present value

(NPV) approach is chosen to investigate the economic competitiveness of EES. The

storage costs, intraday market income, payback obligation, and FCR income are modelled

for the photo years 2020, 2025 and 2030.

The most suitable storage technology for participating in the Belgian CRM auction, and

considered in this research, is Li-ion Battery Energy Storage (BES). This technology has

favourable characteristics such as short response time, high power and energy density,

high round-trip efficiency and low costs. Competing storage technologies are Large-scale

Pumped Hydroelectric Energy Storage (PHES) and Compressed Air Energy Storage

(CAES), however, both are not suitable in Belgium due to geographical restrictions.

The Battery Energy Storage System (BESS) will be deployed in the Frequency

Containment Reserve (FCR) market and intraday market because of the largest revenue

potential. FCR price is not expected to rise in the future and more likely to fall because of

the increase of flexible generation capacity and the further integration of European

ancillary service markets. Three FCR price scenarios are taken into consideration for

calculating the FCR income. The FCR price will be equal to today’s average FCR price

(€ 79.000/MW/year), the FCR price will be equal to the minimum FCR price scenario

observed since July 2019 (€ 51.000/MW/year) and the FCR price will be equal to the

halved today’s average FCR price (€ 39.000/MW/year). The expected intraday market

income is assessed by the software tool Linny-R which applies the optimization technique

linear programming based on the rolling horizon approach. The expected intraday

market income varies from € 16.000/MW/year in 2021 to € 19.000/MW/year from

2030.

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BESS operating only in the intraday market is not expected to be competitive because of

the relatively high required capacity remuneration above € 100/kW/year. Auction

clearing prices in other European CRMs are below € 50/kW/year.

BES does not need capacity remuneration from 2025 onwards with today’s average FCR

price. However, it is not very likely that the FCR price stays the same in the future, as

described above. More realistic is 35% capacity price reduction from the average FCR

price scenario of € 79.000/MW/year to the minimum FCR price scenario of

€ 51.000 /MW/year. BES might be competitive in this scenario with a relatively high de-

rating factor and no payback obligation of the CRM. In the halved today’s average FCR

price scenario, BES will not be competitive in 2025 given the relatively high required

capacity remuneration of minimum capacity remuneration of at least € 69/kW/year. BES

might be competitive in 2030 with the halved today’s average FCR price since the

required capacity remuneration is at least € 21/kW/year. However, the capacity market

might be already saturated with capacity providers bidding near zero in the auctions.

Decisive parameters for competitiveness are the reference price, strike price and the de-

rating factor.

The results of this research are not convincing to regard BES as a competitive technology

in the upcoming Belgium capacity market. Revenue stacking by combining the FCR and

intraday market income would increase the competitiveness of BES and is necessary.

Further research should focus on deployment strategies of BESS in different markets and

FCR price developments.

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Contents

Acknowledgements 3

Executive Summary 4

Contents 7

List of Figures 11

List of Tables 13

1. Introduction 15

1.1 Problem statement 15

1.2 Research objective 17

1.3 Outline 18

2. Literature review 19

2.1 Capacity Remuneration Mechanism 19

2.2 Central buyer mechanism based on reliability options 22

2.2.1 Reliability options mechanisms in Europe 22

2.3 Electrical Energy Storage (EES) 23

2.3.1 Presence of EES on European capacity markets 24

2.4 Competitiveness 26

2.5 Knowledge gap 27

3. Research questions and approach 30

3.1 Research questions 30

3.2 Research approach 31

3.3 Methods 33

3.3.1 Net Present Value 33

3.3.2 Linny-R 34

4. System description 36

4.1 Characteristics of the Belgian CRM 36

4.1.1 Description 37

4.1.1.1 Market reference price and strike price 38

4.1.1.2 Availability requirements and availability penalties 41

4.1.1.3 Clearing algorithm 42

4.1.1.4 Demand curve and price caps 43

4.1.1.5 T-1 reserved volume 43

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4.1.1.6 Minimum participation threshold 43

4.1.1.7 De-rating factors 44

4.1.1.8 Investment levels 46

4.1.2 Conclusions 47

4.2 Appropriate EES technologies 48

4.2.1. Common BES projects 50

4.3 Opportunities in the electricity markets 51

4.3.1 Wholesale electricity market 51

4.3.1.1 Description 51

4.3.1.2 Analysis 53

4.3.2 Ancillary services 54

4.3.2.1 Description 54

4.3.2.1.1 Frequency Containment Reserve 56

4.3.2.1.2 Automatic Frequency Restoration Reserve 57

4.3.2.1.3 Manual Frequency Restoration Reserves 57

4.3.2.2 Analysis 58

4.4 Price developments 61

4.4.1 Intraday market 61

4.4.1.1 Relation between day-ahead and intraday market price 62

4.4.2 FCR 64

5. Model description 65

5.1 Conceptual model 65

5.2 BESS setup 66

5.2.1 Dimensioning 66

5.2.1.1 Intraday market 66

5.2.1.2 FCR market 67

5.2.1 Costs and technical parameters 68

5.3 Intraday market income modelling 69

5.3.1 Description 69

5.3.1.1 Roll and look-ahead period 70

5.4 FCR income modelling 71

5.4.1 Charging costs 73

5.5 CRM contract length 74

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5.6 Scenarios 77

5.6.1 CRM design scenarios 77

5.6.2 Intraday market price scenarios 78

5.6.2.1 Payback obligation 79

5.6.3 FCR price scenarios 80

5.7 Experiment design 81

6 Verification and validation 83

6.1 Verification 83

6.2 Input validation 85

6.3 Model validation 86

7. Results 87

7.2 Competitiveness in the intraday market 88

7.2.1 Doubling the depth of storage 89


7.3 Competitiveness in the FCR market 91

7.3.1 Average FCR price scenario 91

7.3.2 Minimum FCR price scenario 92

7.3.3 50% Average FCR price scenario 93

7.3.4 Revenue stacking of FCR and intraday market income 94

7.4 Conclusions 95

8 Discussion 96

8.1 Results 96

8.1.1 Intraday market income 96

8.1.2 Payback obligation 97

8.1.3 FCR income 98

8.2 Limitations 99

8.2.1 Data 99

8.2.2 Method 99

8.2.2.1 Net Present Value 99

8.2.2.2 Linny-R 100

8.2.3 Model and analysis 100

8.3 De-rating factor methodology 101

8.3.1 Belgian methodology 101

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8.3.2 BES operation strategies 103

8.3.3 UK methodology 105


9 Conclusions 107

9.1 Answers to the sub-questions 107

9.2 Answer to the main research question 110

9.3 Policy recommendations 111

9.4 Recommendations for further research 112

10 Reflection 113

10.1 Scientific relevance 113

10.2 Societal relevance 114

10.3 Link to the EPA programme 114

11 References 115

12. Appendices 133

12.1 Full cycle definition 133

12.2 Intraday market 134

12.2.1 Input prices 134

12.2.2 Income build year 2020 135



12.3 FCR market 137

12.3.1 Cycles and energy delivery on FCR market 137

12.3.2 Price scenarios 140

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List of Figures

Figure 1. Price duration curve of the EPEX SPOT day-ahead market price 2013-2018

(Elyxis, 2019). ................................................................................................................................................ 19

Figure 2. Taxonomy of the capacity mechanism models (European Commission, 2015).

............................................................................................................................................................................. 21

Figure 3. Rolling horizon approach (Lu, Ying, & Chen, 2016). ................................................... 35

Figure 4. Belgian CRM timeline until the first capacity auction (Elia, 2019i)....................... 36

Figure 5. Application of reliability options (Vázquez, Rivier, & Pérez-Arriaga, 2002). .... 38

Figure 6. Price development of Li-ion battery pack price outlook till 2030

(BloombergNEF, 2019a). .......................................................................................................................... 49

Figure 7. Sorted (high-low) difference in Belgian intraday and day-ahead prices of

consecutive hours for a half year between December 4, 2018, and June 4, 2019. (Eneco,

personal information, June 26, 2019). ................................................................................................. 53

Figure 8. Sorted (high-low) difference in Belgian intraday and day-ahead prices of

consecutive hours for a half year between December 4, 2018, and June 4, 2019, < 100

hours. (Eneco, personal information, June 26, 2019). ................................................................... 53

Figure 9. Average day-ahead market price forecasts in Belgium 2020-2030 under

different CO2 and technologies. .............................................................................................................. 62

Figure 10. Hourly VWAP Belgium intraday and day-ahead market prices March 2019. . 62

Figure 11. Hourly VWAP Belgium intraday and day-ahead market prices (per month)

January 2014 - July 2019. .......................................................................................................................... 63

Figure 12. Competitiveness evaluation model. ................................................................................ 65

Figure 15. Linny-R model for calculating the intraday market income. ................................. 69

Figure 17. Marginal capacity price in Belgium on the regional platform between July 1,

2019 – September 29, 2019 (Regelleistung, n.d. -b). ..................................................................... 71

Figure 18. Continental Europe synchronous area frequency 3-3-2018 00:00 - 4-3-2018

0:00 (RTE, 2019). ......................................................................................................................................... 72

Figure 19. Intraday market income (2026) by operating different number of cycles. ..... 75

Figure 20. Aggregated intraday market income for different BES lifetime (years). .......... 76

Figure 16. Payback obligation in 2025 with strike price € 125/MWh. ................................... 79

Figure 21. Verification of Linny-R model of 2026. .......................................................................... 83

Figure 22. Required capacity remuneration, given the day-ahead market income in the

reference scenario and in the optimistic scenario (2020, 2025, 2030). ................................. 88

Figure 23. Breakdown of the EBIDTA for 2020, 2025 and 2030. .............................................. 90

Figure 24. Required capacity remuneration, given the average FCR price in the reference

and optimistic scenario (2020, 2025, 2030). .................................................................................... 91

Figure 25. Required capacity remuneration, given the minimum FCR price in the

reference and optimistic scenario (2020, 2025, 2030). ................................................................ 92

Figure 26. Required capacity remuneration given 50% of the average FCR price in the

reference and optimistic scenario (2020, 2025, 2030). ................................................................ 93

Figure 27. Combining intraday market and FCR incomes. .......................................................... 94

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Figure 28. Process overview of the methodology for determining the de-rating factors of

the Belgian CRM (based on Elia, 2019e). ......................................................................................... 101

Figure 29. Alternative contributions of BES to shortage periods (Based on Dent, Wilson

Zachary, 2017). .......................................................................................................................................... 104

Figure 14. Cycling process (Thirugnanam, Saini., & Kumar, 2012). ..................................... 133

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List of Tables

Table 1. Auction results of battery storage in Irish all-island and United Kingdom

(EirGrid., & SONI, 2018; EirGrid., & SONI, 2019a; EirGrid., & SONI, 2019b; National Grid,

2018a; National Grid, 2018b; National Grid, 2019)........................................................................ 24

Table 2. Current state of scientific knowledge on the relevant subjects for evaluating the

competitiveness of EES in the future Belgian capacity market. ................................................. 29

Table 3. Set of BESS constraints and description (Hu., Chen., & Bak-Jensen, 2010). ......... 34

Table 4. Elements determining the functioning of the CRM (Elia, 2019i). ............................ 37

Table 5. Possible outcomes reliability options (Vázquez, Rivier, & Pérez-Arriaga, 2002).

............................................................................................................................................................................. 39

Table 6: Mentioned strike prices in Italy and Irish all-island CRM (European

Commission, 2018; EirGrid, & SONI, 2019). ...................................................................................... 40

Table 7. SLA categories for energy-constrained capacity providers. Adapted from Elia

(2019k). ........................................................................................................................................................... 41

Table 8. United Kingdom and Irish all-island De-rating factors T-4 capacity auction

(deliver year 2022/23) and Poland de-rating factors capacity auction (delivery year

2021-2022, 2022-2023, 2023-2024) (Rozporządzenie Minstra Energii; 2018; EirGrid., &

SONI, 2019; National Grid, 2018c). ....................................................................................................... 45

Table 9. Investment levels of European CRMs (CREG, 2019). .................................................... 46

Table 10. Specification of the three EPEX SPOT Belgium market segments (EPEX SPOT,

n.d. -e). .............................................................................................................................................................. 52

Table 11. Overview of the (future) ancillary services (Elia, 2017a; Elia 2018d; Elia,

2019b). ............................................................................................................................................................. 55

Table 12. Technical characteristics ancillary services (Elia, 2017a; Elia, 2018a; Elia,

2019b; Elia, 2019c). .................................................................................................................................... 58

Table 13. Average price of selected bids for the ancillary services between January 2019

– week 31 August 2019 (Elia, n.d. –c). ................................................................................................. 60

Table 14. BESS setups taken initially into consideration. ............................................................ 67

Table 15. Li-ion BESS costs parameters and values (IRENA, 2017; Eneco, personal

information, May 21, 2019; Eneco, personal information, August 2, 2019; XE, 2019;

Statbel, 2019; KBC, 2018). ........................................................................................................................ 68

Table 16. Investment levels of European CRMs (CREG, 2019). ................................................. 74

Table 17. Reference scenario and experiments for calculating the required capacity

remuneration. ................................................................................................................................................ 77

Table 18. Average day-ahead market price 2018 predictions for 2020, 2023, 2025, 2028,

2030 including percentage change. ...................................................................................................... 78

Table 19. Payback obligation with strike price € 125/MWh (2021-2030). .......................... 79

Table 20. Year description of scenarios 2020, 2025 and 2030. ................................................. 81

Table 21. Experiments for BESS (10 MW/10 MWh) operating in the intraday market. .. 81

Table 22. Experiments for BESS (12,5 MW/10 MWh) operating in the FCR market. ....... 82

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Table 23. Experiments for BESS (12,5 MW/10 MWh) operating in both the intraday and

FCR market. .................................................................................................................................................... 82

Table 24. Published (approximated) project costs by developers compared to IRENA’s

estimated storage costs (Infigen, 2018; Enel, 2019; Endesa, 2017; IRENA, 2017). ........... 85

Table 25. Income differences in the intraday market and the FCR market (2020, 2025,

2030). ................................................................................................................................................................ 95

Table 26. Deals on the EPEX intraday market 1-1-2018 02:00. ................................................ 96

Table 27. Influence of the payback obligation on the required capacity remuneration. . 97

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1. Introduction

1.1 Problem statement

The worldwide energy consumption should drastically change the coming decennia to

avoid dangerous climate change. In 2015, 175 parties signed the Paris Agreement to keep

the global temperature rise this century below 2 degrees Celsius, aiming for 1,5 degrees

Celsius (UNFCCC, 2015). In the same year, all the United Nations Member States adopted

the Sustainable Development Goals to provide affordable and clean energy for everyone

by 2030 (United Nations, 2015). Every country in the world faces the major challenge of

reducing greenhouse gas emissions.

Renewable energy sources, such as wind and solar energy, offer great opportunities to

make the energy supply more sustainable. However, the energy production of these

sources strongly depends on weather conditions. Besides, the energy consumption varies

per part of the day but also per season. The difference between the supply profile and the

energy demand is considered as a major challenge for governments, utility companies,

and transmission system operators (TSOs). Energy storage is considered as one of the

major components of the solution to deal with the variable nature of wind and solar

energy. By way of illustration, the world demand for battery storage is estimated to reach

2800 gigawatt-hours (GWh) in 2040 – corresponding to storing around half of 2019’s

global renewable energy production in a day (World Bank, 2019).

The transition to a low-carbon society with higher penetration levels of variable

renewables is not the only challenge for Belgium. On January 31, 2003, the Belgian

parliament approved the bill for the nuclear power phase-out, implying shutting down all

seven nuclear power plants. No new nuclear power plants will be built for industrial

electricity production (Belgisch Staatsblad, 2003). In the end, no electricity is generated

from nuclear energy in 2025. Belgium significantly depends on nuclear energy, as nuclear

power plants produce more than half of Belgium’s total electricity production in 2017.

Nuclear represented 34% of Belgium’s electricity production in 2018 because of

unavailability of nuclear multiple power plants, which led to a greater import of

electricity from abroad (Elia, 2019a).

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The Belgian minister of Energy has commissioned the Belgian TSO Elia to investigate the

urgency of adequacy and flexible electricity sources to ensure the security of supply in

the period 2017-2027. Elia cannot guarantee that the current energy-only market,

combined with a strategic reserve mechanism, can ensure sufficient investments in

generation capacity to maintain the security of supply in Belgium (Elia, 2016a). The

proposed measure is a Capacity Remuneration Mechanism (CRM) based on reliability

options and should succeed the strategic reserve mechanism (Belgische Kamer van

volksvertegenwoordigers, 2019a).

The objective of the prospective Belgian CRM is to ensure a certain level of security of

supply at the lowest possible cost. Capacity providers are remunerated in auctions for

the availability of their capacity per megawatt (MW) in the energy market (Belgische

Kamer van volksvertegenwoordigers, 2019a). On April 4, 2019, the Belgian federal

parliament approved the bill for the adaption of the Belgian electricity act that allows the

introduction of a CRM to encourage investments in capacity (Belgische Kamer van

volksvertegenwoordigers, 2019a). According to Elia’s recent adequacy and flexibility

study, the new capacity required is 3,9 GW from 2025 to guarantee the security of supply

after a full nuclear power phase-out. The assumption takes into account the limited

options during the winter months because of planned coal exits of neighbouring countries

and in particular Germany (Elia, 2019d). There will be one auction four years ahead of

each delivery year and one auction a year ahead of each delivery year securing the

capacity required (Belgische Kamer van volksvertegenwoordigers, 2019a). The first

auction should take place in October 2021 to ensure sufficient capacity from November

2025 (Elia, 2019d).

The capacity auctions do not exclude any form of capacity because of the technological

neutrality requirement imposed by the European Commission. Each capacity provider

who can contribute to the security of supply can participate in the auction. Possible

candidates are power plants, demand response, storage, and renewable sources

(Belgische Kamer van volksvertegenwoordigers, 2019b). In the light of the world’s

energy transition to a sustainable energy future is whether storage is competitive with

(existing) conventional plants in the Belgian capacity auction.

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1.2 Research objective

Eneco is one of the largest utility companies in the Belgian electricity market for

consumers (VREG, 2019). In 2019, Eneco has a substantial generation capacity of on- and

offshore wind energy and solar energy (resp. 436 MW, 178 MW, 78 MW) in Belgium but

no flexible capacity (Eneco, n.d. –a, Eneco, n.d. –b, Eneco, n.d. –c). This implies Eneco is

more sensitive to high electricity prices when the sun does not shine, and the wind does

not blow (Eneco, personal communication, April 25, 2019). Electrical Energy Storage

(EES) can reduce the risk of financial losses during high electricity prices and aligns

Eneco’s mission (‘everyone’s sustainable energy’) better than conventional power plants

(Eneco, 2019). Also, EES can be seen as an additional income-generating asset next to

Eneco’s existing wind and solar assets in Belgium. Therefore, Eneco considers

participating in the Belgian capacity auction with EES capacity.

The research objective is to evaluate the competitiveness of EES installed by Eneco in the

future Belgian capacity market. The long-term capacity remuneration, possible costs

related to the capacity market, storage costs, and revenue derived in the electricity

market are integrated into one analysis.

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1.3 Outline

This research is structured as follows:

● Chapter 2 includes the literature review. The literature review explains the

scientific problem regarding EES in the future Belgian capacity market. This

section discusses the core concepts and the formulation of the academic

knowledge gap;

● Chapter 3 involves the main research question, sub-questions and the research

approach including the methods used;

● Chapter 4 entails the system description, discusses the characteristics of the

Belgian CRM and explorers appropriate EES technologies. Subsequently, the

chapter discovers opportunities in the Belgian electricity market and discusses

price developments in the electricity market;

● Chapter 5 includes the model description including the conceptual model, BESS

dimensiong, intraday market and FCR income modelling and the scenarios;

● Chapter 6 discusses verification and validation;

● Chapter 7 presents the results including the required capacity remuneration;

● Chapter 8 discusses the results and the limitations of this research;

● Chapter 9 presents the conclusions, policy recommendations and

recommendations for further research;

● Chapter 10 entails the reflection on the scientific relevance, societal relevance and

the link to the EPA programme;

● Chapter 11 includes the references;

● Chapter 12 involves the appendices;

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2. Literature review

The core concepts of the research will be discussed and defined in this chapter.

Eventually, the knowledge gap in existing research will be addressed.

2.1 Capacity Remuneration Mechanism

The ideal situation would be to have an electricity market which ensures the security of

supply for the lowest consumer prices. Generation capacity investments might lead to

more competitive market results and maintain the reliability of the electrical grid.

However, power generation companies have to deal with demand uncertainty and

competition. Besides, investments are to a large extent sunk costs, and generators have

to make the investment decision at the right time (Genc, 2012). The investment in

generation capacity is profitable if the expected revenue during production hours are at

least equal to the investment costs (Mulder, 2017). The revenue of peak generation assets

significantly depends on the frequency, height, and duration of price spikes (De Vries,

2007). Figure 1 shows the price duration curve of the hourly day-ahead market price on

the Belgian EPEX SPOT exchange between 2013-2018 (Elyxis, 2019).

Figure 1. Price duration curve of the EPEX SPOT day-ahead market price 2013-2018 (Elyxis, 2019).

0

100

200

300

400

500

600

700

Be

lgia

n d

ay

-ah

ae

d m

ark

et

pri

ce [

€/

MW

h]

Duration [% of the time]

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According to the underlying day-ahead market prices of the price duration curve, the

price rises only about 3% of the time above 100 euros per megawatt-hour (MWh). Small

differences in the occurrence of price spikes greatly affect the steepness of the price

duration curve and thus the expected profitability of generation capacity investments (De

Vries, 2007). Peak plants should recover their investment costs from these price spikes

(Cepeda & Finon, 2011). However, peak prices are subject to the political debate because

the introduction of price caps should protect consumers from prices higher than their

value of lost load (de Vries, Correljé, & Knops, 2017). The situation where the revenue

from the energy markets is insufficient for a generation asset to cover its capital and

operating expenses is called the ‘missing money’ problem (Newbery, 2016).

If the market provides insufficient generation capacity to meet the demand without

government intervention, the solution involves the introduction of additional

mechanisms to ensure the security of supply (Batlle & Pérez-Arriaga, 2008). This

situation applies to Belgium, where the energy-only market cannot provide proper

investments incentives to ensure the security of supply after 2025 (Elia, 2019d). The

European Directive 2003/54/EC has been implemented which provide designations for

transmission and distribution system operators for direct interventions in the electricity

market (Finon & Pignon, 2008). This development brings the introduction of capacity

(remuneration) mechanisms (CRM) into the spotlight. The objective of all capacity

mechanisms is to introduce incentives for utility companies to provide sufficient

generation capacity, even in the situation of imperfect information or concerns regarding

risk. Besides, it should stimulate to maximise utility companies’ generation output during

shortages in the electricity market (de Vries, Correljé, & Knops, 2017). Capacity

mechanisms differ from one another in structure and implementation. All mechanisms

each have their effects in the electricity market, and each country’s implementation is

unique (Meulman & Méray, 2012).

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The capacity mechanisms can be divided into five types according to the European

Commission (2015). The first classification is based on whether the mechanism is

volume-based or price-based. Price-based means the TSO sets the price, and the volume

depends on the willingness of investors. The opposite of price-based is volume-based

where the TSO sets the volume, and market forces determine the price. Market-wide

capacity mechanisms reward all forms of capacity, whereas targeted capacity

mechanisms only reward a subset (Hancher, de Hauteclocque, & Sadowska, 2015). The

volume-based capacity mechanisms can be divided into the following subgroups;

targeted and market-wide. All five types of capacity mechanisms are presented in Figure

2. The type of capacity remuneration mechanism in the Belgian bill, and therefore

relevant in this research, is the (market-wide) central buyer capacity mechanism based

on reliability options, which is the red block in the figure below. The mechanism will be

explained in 2.2.

Figure 2. Taxonomy of the capacity mechanism models (European Commission, 2015).

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2.2 Central buyer mechanism based on reliability options

Reliability contracts should provide capacity providers with better incentives for being

active in the energy market during scarcity (De Vries, 2007). The central buyer

mechanism based on reliability options has been first introduced by Pérez-Arriaga

(1999) and further elaborated by Vázquez, Rivier, & Pérez-Arriaga (2002). It is often

called in literature reliability contracts, reliability options mechanism and energy call

options auction (Bidwell, 2005; Vázquez, Rivier, & Pérez-Arriaga, 2002; De Vries, 2007;

Vázquez, Batlle, Rivier, & Pérez-Arriaga, 2003; Bezerra et al., 2006; Bezerra, Barroso, &

Pereira, 2011).

The regulatory authority of the energy market, e.g., the TSO, prescribes the market to

purchase reliability contracts, which represents the desired volume of capacity. The

reliability contracts are procured in a competitive auction and are composed of a financial

call option and a penalty for non-delivery of the auction item. The capacity providers

receive a fixed remuneration for the awarded capacity in the auction. The call option of

reliability contract entails that the contracted capacity provider should pay the positive

difference between a reference price, e.g., electricity market price, and a predetermined

strike price during the contracted period in return (often called the ‘payback obligation’).

Capacity providers are penalized if they do not meet the delivery obligation. An elaborate

explanation of the Belgian reliability options mechanism will be discussed in 4.1.

2.2.1 Reliability options mechanisms in Europe

Reliability options schemes have been approved by the European Commission in the

United Kingdom (only Northern Ireland), Ireland and Italy in 2017 and 2018 (European

Commission, n.d.). The electricity market on the island of Ireland, which refers to the

Republic of Ireland and Northern Ireland, is managed and regulated collectively by the

TSOs of the Republic of Ireland (‘EirGrid’) and Northern Ireland (‘SONI’) (European

Commission, 2017a). The jointly CRM of Ireland and Northern Ireland1 is hereafter

referred to as Irish all-island CRM.

1The Irish all-island electricity market operates as a single market under the same market wholesale electricity market arrangement Integrated Single Electricity Market (I-SEM) and capacity is reimbursed through one CRM.

23

2.3 Electrical Energy Storage (EES)

Electrical Energy Storage (EES) refers to the process of transforming electrical energy

from the electricity distribution network into any form allowing temporary storage and

transforming back to electrical energy when needed. The process makes it possible to

store electricity during periods of low demand and supplying energy to the grid during

periods of high demand or shortages (Chen, Cong, Yang, Tan, & Ding, 2009).

The characteristics of EES differ from conventional and non-conventional power plants,

such as gas-fired generation assets and wind turbines. A major feature of EES is the short

response time compared to non-conventional and conventional power plants. As an

example, the response times of gas generation assets are between 4-45 minutes in hot

starts (4-250 minutes in cold starts) and the response times of coal generation assets are

between 100-300 minutes in hot starts (450-900 minutes in cold starts) (Gonzalez-

Salazar, Kirsten, & Prchlik, 2018). In contrast, the response time of chemical storage, such

as Li-ion batteries, is a few milliseconds (Evans, Strezov, & Evans, 2012). The short

response time makes EES suitable for applications which require this feature, such as

ancillary services.

EES technologies differ primarily in efficiency, response time, lifetime, energy and power

capacity, and discharge time (Poullikkas, 2013). The storage characteristics of new

installations improve over time as research on material science continues, and capital

costs are coming down (Dvorkin et al., 2017).

The most common EES application in the world is (electric) energy time-shifting and

accounts for 85% of the total EES applications (US DOE, 2017). Energy time-shifting

involves buying and storing of (electric) energy when demand and energy prices are low

and selling and releasing the energy when demand and energy prices are high (Energy

Storage Association, n.d.-a). The objective is to find the most economically favourable

buying and selling moments. Other applications are electric supply capacity (4%), black

start (4%) and renewables capacity firming (3%) (US DOE, 2017).

24

2.3.1 Presence of EES on European capacity markets

Reliability options schemes are approved by the European Commission for the Irish all-

island CRM and Italian CRM, as mentioned in 2.2.1. EES has not been successful in the

Irish-island T-1 auctions (auction one year ahead the deliver year) in December 2017 and

2018, but has been successful in the T-4 auction (auction four years ahead the delivery

year) in May 2019 (EirGrid., & SONI, 2018; EirGrid., & SONI, 2019a; EirGrid., & SONI,

2019b). The auction clearing price, the awarded de-rated battery capacity2 in the CRM

auction and the share of battery capacity of the total de-rated capacity for the Irish all-

island capacity auctions are presented in Table 1. According to the Italian authority, the

first capacity auction should take place before end of 2019, so there are no results yet

(Terna 2019; Askanews, 2019).

CRM Type Date Deliver year Auction

clearing price

[€/kW/year]

Awarded de-

rated battery

capacity [MW]

Share of total

de-rated

capacity [%] Irish all-

island T-4 May. 2019 2022/23 46,15 81 1,1 T-1 Dec. 2018 2019/20 40,65 0 0 T-1 Dec. 2018 2018/19 41,80 0 0

United

Kingdom T-1 Jun. 2019 2019/20 0,87 (£ 0,77) 23 0,6 T-4 Feb. 2018 2021/22 9,52 (£ 8,40) 158 0,3 T-1 Feb. 2018 2018/19 6,80 (£ 6,00) 113 1,9

Table 1. Auction results of battery storage in Irish all-island and United Kingdom (EirGrid., & SONI, 2018; EirGrid., & SONI, 2019a; EirGrid., & SONI, 2019b; National Grid, 2018a; National Grid, 2018b; National Grid, 2019).

2 The de-rated capacity awarded in the CRM auction, which is the contributing to generation adequacy, is equal to the awarded nominal capacity times the relevant de-rating factor for the capacity provider (see 4.1.1.7).

25

The CRM in the United Kingdom differs from the upcoming Belgian CRM and existing Irish

all-island and Italian CRM because it not based on reliability options, which implies

United Kingdom’s CRM does not include the payback obligation for capacity providers.

However, a lot of battery storage participate in United Kingdom’s capacity auctions and

therefore relevant to take into account. Battery storage projects have broken through in

the United Kingdom and awarded since the T-4 capacity auction for 2021-2022 in

December 2016 (National Grid, 2016). Full details of awarded battery capacity for

T-1 auction on June 2019, T-4 auction and T-1 auction in February 2018 are presented in

Table 1.

Remarkable is the significant difference in auction clearing price between the Irish all-

island and United Kingdom’s capacity auctions, and especially the latest T-1 auction in

the United Kingdom with a clearing price near zero. This can be explained by the fact that

T-1 auction is a ‘top-up’ auction of the main T-4 auction to meet the additional capacity

demand of that particular deliver year. Therefore, less capacity is procured in the T-1

auction compared to the T-4 auction of the delivery year 2019-2020, respectively 3,7 GW

versus 46 GW (National Grid, 2015; National Grid, 2019). The clearing price near zero can

be explained by the large supply of (cheap) capacity. 9,4 GW entered the latest T-1

capacity auction, including 198 MW of battery storage, and only 38% was awarded. One

new battery project and five existing battery projects are awarded, which in total

accounted for only 0,6% of the total capacity. The big winners in this specific auction are,

mostly existing, Combined Cycle Gas Turbine (CCGT), Open Cycle Gas Turbine (OCGT) and

Combined Heat and Power (CHP) which accounts in total over 40% of the awarded

capacity. The supply curve of the auction clearly shows a lot of capacity providers are

willing to bid close to zero (National Grid, 2019). The example of the United Kingdom

underlines the fierce competition in the capacity auction between technologies and new

and existing assets.

26

2.4 Competitiveness

The conceptual term competitiveness has multiple meanings and depends upon context.

Here, it is in the context of the economic feasibility of EES participating in the future

central buyer mechanism based on reliability options in Belgium.

In theory, any capacity provider can be successful by bidding € 0/kW/year in the capacity

auction, assuming that he meets all conditions of the CRM. However, an economically

rational capacity provider would participate in the CRM if his expected costs are smaller

than his expected revenue from being active in the capacity market. From the perspective

of this research, the costs consist of the storage costs and possible costs arising from

participating in the CRM, i.e., payback obligations and penalties. The expected revenue

consists of the revenue derived in the electricity market and the capacity remuneration.

Thus, the competitiveness of EES in the future Belgian capacity market can be expressed

by the following inequality:

𝐶𝑠𝑡𝑜𝑟𝑎𝑔𝑒 + 𝐶𝑐𝑟𝑚 < 𝑅𝑚𝑎𝑟𝑘𝑒𝑡 + 𝑅𝑐𝑟𝑚

As mentioned in 2.2, the missing money problem occurs if the revenue from the energy

markets is insufficient for a generation unit to cover its capital expenses and operating

expenses. Given the occurrence of the ‘missing money’ problem, the inequality above

implies the capacity remuneration 𝑅𝑐𝑟𝑚 a capacity provider requires should be greater

than the missing money (i.e., the revenue derived in the electricity market minus the

storage costs 𝐶𝑠𝑡𝑜𝑟𝑎𝑔𝑒 and the possible costs arising from participating in the mechanism

𝐶𝑐𝑟𝑚.) Otherwise, there’s no economic incentive to participate in the market. This

inequality is expressed by the equation below:

𝐶𝑠𝑡𝑜𝑟𝑎𝑔𝑒 + 𝐶𝑐𝑟𝑚 − 𝑅𝑚𝑎𝑟𝑘𝑒𝑡 < 𝑅𝑐𝑟𝑚

The four cost and revenue components will be defined in the system description.

27

2.5 Knowledge gap

As mentioned in 2.2, much is known about capacity remuneration mechanisms in general

and in particular the central buyer mechanism based on reliability options. However, no

scientific research has been conducted on the performance of EES on a capacity market.

Over the past several years, a lot of research has been done on the role of utility-scale EES

and identifying suitable technologies, given the increasing presence of Renewable Energy

Sources (RES) (Ferreira, Garde, Fulli, Kling, & Lopes, 2013; Soloveichik, 2011; Dunn,

Kamath & Tarascon, 2011; Ibrahim, Ilinca, & Perron, 2008; Divya, & Østergaard, 2009).

The literature review showed that the most appropriate choice for an EES technology

highly depends on the application. It is nearly impossible to give one simple,

straightforward answer. Research has demonstrated that EES could contribute to the

security of supply by, for example, lowering peak demand (Zhou, Mancarella, & Mutale,

2015). Methodologies have been introduced to quantify the contribution of an EES unit

to the security of supply and estimate its capacity value, i.e., the quantification of the EES

unit’s effect on the system reliability (Zhou, Mancarella, & Mutale, 2015; Sioshansi,

Madaeni, & Denholm, 2013; Konstantelos, & Strbac, 2018; Evans, Tindemans, & Angeli,

2019). However, scientific research lacks on the contribution of EES in capacity auctions

and related market-specific rules.

The Belgian Commission for Electricity and Gas Regulation (CREG) and the Belgian

federal government service FOD Economy carried out the most recent research of the

possibilities of EES on specifically the Belgian electricity market in 2015 (CREG, 2015;

FOD Economie, 2015). At that time, Pumped Hydroelectric Energy Storage (PHES) had

the highest maturity in the study by FOD economy because of its high efficiency and high

energy density, which makes it suitable for balancing energy supply and demand across

the electric grid. Both studies explicitly mentioned that technology costs of storage could

fall substantially in the coming years due to technological advances and economies of

scale. Therefore, the results should be interpreted with caution because the findings are

based on technology cost data from more than five years ago and are a poor indication of

the economic feasibility of EES.

28

Two papers by Bezerra et al. (2006) and Bezerra, Barroso, & Pereira (2011) describe

bidding strategies for capacity providers participating in Brazilian reliability options. The

research focusses on calculating the optimal strike price bid, and option premium bid to

maximise the capacity provider’s revenue, given a desired risk-adjusted rate of return.

However, the described stochastic optimisation in the papers is less accurate for the

European central buyer mechanism based on reliability options. First, the Brazilian

reliability options mechanism differs from the European reliability options mechanism

because both the premium fee and the strike price should be proposed by the capacity

providers in the Brazilian auction. Second, the expected market revenue is calculated

using Monte Carlo simulation for different hydrological scenarios (Bezerra, Barroso, &

Pereira, 2011). The Brazilian power system is hydro-based, so the hydrological

conditions influence the market reference price (Bezerra et al., 2006). However, they are

less relevant in Europe since the European power system is not hydro-based. Besides, the

calculation in the research for the expected premium fee per year assumes that a fraction

of the hours per year the market reference price exceeds the strike price (Bezerra et al.,

2006; Bezerra, Barroso, & Pereira, 2011). Estimates can be made more accurate by

calculating the payback obligation based on market reference price forecasts.

Last, the variable market revenue (or losses) are calculated by subtracting the

operational costs from the strike price (Bezerra et al., 2006; Bezerra, Barroso, & Pereira,

2011). This implies the strike price is the highest price the capacity provider can receive

in the market. This does not have to be true since it might be possible the market

reference price is not related to the market where the capacity provider is active. For

example, the market reference price could be the day-ahead market price, and the

capacity provider receives revenue from ancillary services. The ancillary service market

prices are relatively higher compared to day-ahead market prices (Regelleistung, n.d -a.;

EPEX SPOT, n.d. -a). This means the capacity provider can earn more than the strike price

imposed by the TSO if the reference price is based on the day-ahead market price.

29

Based on outdated costs of EES technologies, lack of specific focus on the European

reliability options mechanism and the role of storage, no conclusion can be drawn as to

whether EES is competitive in the future Belgian capacity market. The findings of this

study for Belgium could help capacity providers in different countries whether they

should participate in an auction of a central buyer mechanism based on reliability

options.

Table 2 presents the current state of scientific knowledge on the relevant subjects for

evaluating the competitiveness of EES in the future Belgian capacity market.

Subject Scientific knowledge

None - little – sufficient – much

References

Central buyer mechanism based

on reliability options

Sufficient Bidwell (2005), Vázquez, Rivier, &

Pérez-Arriaga (2002), De Vries (2007),

Vázquez, Batlle, Rivier, & Pérez-

Arriaga (2003), Bezerra et al. (2006),

Bezerra, Barroso, & Pereira (2011)

Role of EES and increasing

(intermittent) RES capacity

Much Ferreira, Garde, Full, Kling, & Lopes

(2013), Soloveichik, (2011), Dunn,

Kamath, & Tarascon, (2011), Ibrahim,

Ilinca, & Perron (2008), Divya, &

Østergaard (2009)

Contribution of EES to the

security of supply

Sufficient Zhou, Mancarella, & Mutale (2015), Konstantelos, & Strbac (2018), Evans,

Tindemans, & Angeli (2019),

Sioshansi, Madaeni, & Denholm,

(2013)

Competitiveness (in central

buyer mechanism based on

reliability options)

Little Vázquez, Rivier, & Pérez-Arriaga

(2002) Bezerra et al., (2006), Bezerra,

Barroso, & Pereira (2011)

Market rules for EES

participating in capacity

auctions

None

Table 2. Current state of scientific knowledge on the relevant subjects for evaluating the competitiveness of EES in the

future Belgian capacity market.

30

3. Research questions and approach

3.1 Research questions

Based on the identified knowledge gap and research objective, the main research

question of the research can be formulated as follows:

What is the competitiveness of electrical energy storage in the future Belgian

capacity market?

And the following sub-questions need to be answered to answer the main research

question:

1. What are the characteristics of the Belgian Capacity Remuneration Mechanism?

2. Which EES technologies fit the characteristics of the Belgian capacity market best?

3. What is the revenue derived from the wholesale electricity market and the

potential payback obligation of the capacity remuneration mechanism?

4. What is the revenue derived from ancillary services?

5. What is the remuneration required for EES in Belgian capacity remuneration

mechanism auction?

31

3.2 Research approach

The research objective is to evaluate the competitiveness of EES in the future Belgian

capacity market by providing a systemic view of the problem. The first two sub-questions

of this study are qualitative in nature, and the last three sub-questions are quantitative in

nature. The relevant methodology to fulfil the objective of this study and answer the

research questions is mixed methods research. Mixed methods refer to the integration, or

‘mixing’ of both qualitative and quantitative data in one study. The idea behind the

methodology is that the combination of qualitative and quantitative approach leads to a

better understanding of the research problem than only one of the two approaches is

applied (Creswell, 2014). The relevant research approaches and research methods used

in this study will be discussed per sub-question.

What are the characteristics of the Belgian Capacity Remuneration Mechanism?

The question results in an overview of the Belgian CRM characteristics. The

characteristics will be identified through literature review and desk research. Literature

can help to understand the general functioning of CRMs. Desk research includes research

reports, consultation documents, and Belgian and European laws and legislations from

actors such as the CREG, Elia, the Belgian government and the European Commission.

Which EES technologies fit the characteristics of the Belgian capacity market best?

The most suitable EES technology in the future Belgian capacity market, and taken into

consideration in this research will be treated with the above question. Literature review

and desk research are suitable methods for identifying appropriate EES technologies in

the Belgian capacity market. Literature is relevant for comparing the characteristics of

the different EES technologies. Desk research includes studies on storage costs of energy

research agencies and reports on the suitability of specific technologies in Belgium.

32

What is the revenue derived from the wholesale electricity market and the


First, the wholesale electricity market with the greatest economic potential for the

considered EES technology from the previous sub-question will be determined.

Subsequently, the potential revenue and payback obligation are calculated under

different CRM design and price scenarios. The relevant research methods are desk

research for creating electricity market and payback obligation scenarios. The expected

intraday market income is assessed by the software tool Linny-R which applies the

optimization technique linear programming. Historical wholesale electricity market

prices obtained via Eneco will be used as input of the optimization model. The scenarios

will be further discussed in the 5.6 and Linny-R in 3.3.2.

What is the revenue derived from ancillary services?

Similar to the previous sub-question, the ancillary service with the greatest economic

potential for the considered EES technology will be determined. The potential revenue

will be calculated again under different CRM design and price scenarios. The appropriate

research method is desk research for creating ancillary service price scenarios, and Excel

will be used for analysing historical FCR prices and making relevant calculations.

Historical FCR prices are obtained from the ancillary services auction platform

Regelleistung.

What is the remuneration required for EES in Belgian capacity remuneration

mechanism auction?

Eventually, the required capacity remuneration will be calculated and based on the

revenue from the wholesale market or ancillary services, storage costs and the payback

obligation. The required capacity remuneration will be determined by Excel-based

financial modelling, using the Net Present Value (NPV), which will be further explained

in 3.3.1.

33

3.3 Methods

3.3.1 Net Present Value

Financial modelling is a meaningful methodology for decision-makers to offer business

solutions (Ho, & Lee, 2004). The financial modelling method Net Present Value (NPV) is

chosen to investigate the economic competitiveness of BES. The method is the most

commonly used for evaluating investments. The NPV is the sum of the cash inflows and

outflows discounted to the present values (Arnaboldi, Azzone, & Giorgino, 2014).

The formula of the NPV in this research is presented below. The income and costs are

calculated at each point in time 𝑡 over timespan 𝑁 and discounted at the discount rate 𝑟𝑟

(Belderbos, Delarue, & D'haeseleer, 2016). The cash flows taken into consideration in this

research include the earnings before interest, taxes, depreciation, and amortisation

(EBITDA) and the corporate tax. Thus, income revenue derived in the electricity market

𝑅𝑚𝑎𝑟𝑘𝑒𝑡, the capacity remuneration 𝑅𝑐𝑟𝑚, operations and maintenance (O&M) costs for

BES 𝐶𝑜&𝑚 and possible costs arising from participating in the CRM 𝐶𝑐𝑟𝑚. The capital

expenses (CAPEX) 𝐼0 are not discounted to today’s values in this research. The size of the

storage project in this research can be realized within a year. The total capital expenses

(CAPEX) of the EES system will be made at the beginning of the project (𝑡 = 0) and there

is no debt financing required. Therefore, CAPEX are not discounted (Arnaboldi, Azzone,

& Giorgino, 2014). A positive NPV will result in a net profit which means the investment

is acceptable. On the other hand, a negative NPV will result in a net loss, and the

investment should be rejected (Arnaboldi, Azzone, & Giorgino, 2014).

𝑁𝑃𝑉 = − 𝐼0 + ∑𝑅𝑚𝑎𝑟𝑘𝑒𝑡 + 𝑅𝑐𝑟𝑚 − 𝐶𝑜&𝑚 − 𝐶𝑐𝑟𝑚 − 𝐶𝑡𝑎𝑥

(1 + 𝑟𝑟)𝑡

𝑁

𝑡=1

The required capacity remuneration can be derived by complying with the condition that

the NPV should be greater than 0.

34

3.3.2 Linny-R

The expected wholesale market income is assessed by the software tool Linny-R which

applies the optimization technique linear programming. Linear programming involves

the minimising or maximising a linear objective function given certain equality and

equality constraints (Karloff, 2008). The program, developed by Dr. P.W.G. Bots,

determine the most appropriate operation schedule of BES in order to maximize profit

(Henriques, & Stikkelman, 2017). Thus, it calculates the optimal moments of buying and

selling electricity in the wholesale market. The objective function is shown below and is

subject to a set of constraints shown in Table 3.

max ∑ 𝑃𝑡 ∗ 𝐼𝐷𝑡

𝑇

𝑡=1

Constraints Description

𝑇 Planning horizon

𝑃𝑡>0 Charging power 𝑃 at hour 𝑡

𝑃𝑡<0 Discharging power 𝑃 at hour 𝑡

𝐸𝑡+1 = 𝐸𝑡 + η ∗ 𝑃𝑡 ∗ ℎ Energy capacity stored expressed for charging (𝑃𝑡>0) at hour 𝑡 and roundtrip

efficiency η

𝐸𝑡+1 = 𝐸𝑡 + 𝑃𝑡 ∗ ℎ Energy capacity stored expressed for discharging (𝑃𝑡<0) at hour 𝑡

𝐼𝐷𝑡 Wholesale market price at hour 𝑡

−𝑃𝑚𝑎𝑥 ≤ 𝑃𝑡 ≤ 𝑃𝑚𝑎𝑥 Charge-discharge power capacity at hour 𝑡 should be less than the maximum

power capacity of the BESS

0 ≤ 𝐸𝑡 ≤ 𝐸𝑚𝑎𝑥 Charge-discharge energy capacity at hour 𝑡 should be less than the maximum

energy capacity of the BESS

Table 3. Set of BESS constraints and description (Hu, Chen., & Bak-Jensen, 2010).

The optimisation of the charge-discharge decisions, and so the prediction of the

wholesale market income, in Linny-R is based on the rolling horizon approach (Bots,

2017). The approach uses currently known information and short-term forecasts which

has a higher level of reliability to solve the optimisation problem while retaining the

effectiveness of the calculation (Lu, Ying, & Chen, 2016). The rolling horizon approach is

commonly used for scheduling problems dealing with uncertainty such as inventory

control and traffic forecasts (Silvente, Kopanos, Dua, & Papageorgiou, 2018; Lu, Ying, &

Chen, 2016).

35

The rolling horizon approach is shown in Figure 3. The total planning horizon 𝑇 is split

up into 𝑁 stages where each stage 𝑛 contains 𝑚 intervals (where 𝑚 ≥ 1). Each stage 𝑛

consists of a roll period 𝑙, which represents reliable (short-term) forecasts and a look-

ahead period 𝑚 − 𝑙 which represents less reliable (medium-term) forecasts. The model

computes successive stages the roll period 𝑙 while taken into account the entire stage 𝑛.

Thereafter, the stage 𝑛 moves forward by 𝑙 time steps until stage 𝑛 + 1, both forecasts

will be updated and the procedure is done for all 𝑁 stages. It should be taken into

consideration that longer optimisation periods, and in particular longer the look-ahead

periods, which consists of medium-forecasts, decrease the reliability of the overall

prediction (Lu, Ying, & Chen, 2016).

Figure 3. Rolling horizon approach (Lu, Ying, & Chen, 2016).

36

4. System description

This chapter will first describe the characteristics of the Belgian CRM based on what is

known is so far. Thereafter the appropriate EES technologies on the Belgian capacity

market will be discussed. Lastly, the chapter describes the Belgian wholesale electricity

market and ancillary services and focusses on relevant market developments.

4.1 Characteristics of the Belgian CRM

The Belgian bill regarding the CRM has been adopted on April 4, 2019 (Belgische Kamer

van volksvertegenwoordigers, 2019a). The Belgian CRM should comply with the

European Union state aid rules, and the European Commission is expected to approve the

mechanism in June 2020. The prequalification period starts June 2021, and the first

auction (T-4) is planned in October 2021 to ensure sufficient capacity from November

2025 (Elia, 2019i; Elia, 2019d). Figure 4 presents the timeline until the first capacity

auction of the Belgian CRM. It can be concluded from the timeline that the final CRM

market rules and auction parameters will be published after writing this research.

Therefore, this section describes first the characteristics of the Belgian CRM from what is

known from the bill and reports from Elia meetings. It focusses on the most important

parameters within the scope of this research for determining the competitiveness of EES.

Subsequently, assumptions will be made for these parameters based on proposals from

CRM Task Force (stakeholder) meetings organized by Elia, CRM design notes and

parameters from European Commission approved CRMs in the United Kingdom, Ireland,

Italy and Poland (Elia, 2019h; European Commission, n.d.).

Figure 4. Belgian CRM timeline until the first capacity auction (Elia, 2019i).

37

4.1.1 Description

The capacity auction of the CRM is a competitive process in which capacity providers offer

a price for the provision of capacity. Capable capacity providers are existing and future

electricity generators, storage facilities, demand-side response and foreign capacity

(Belgische Kamer van volksvertegenwoordigers, 2019a). Two auctions will be organized

by Elia for each capacity delivery period: one auction four years ahead of each delivery

year (T-4 auction) and one auction a year ahead of each delivery year (T-1 auction). Elia

is responsible for the annual determination of the capacity required and the auction

parameters for the T-4 and the T-1 auctions, whereas the CREG monitors the CRM. The

volume report consists of the calculations of the total capacity required and the number

of hours during which the capacity will be used for the sake of adequacy to ensure the

security of supply. The parameter report includes the demand curve, price limit(s),

market reference price, strike price, and de-rating factors.

Table 4 provides an overview of the most important elements of the CRM that determines

the functioning. All the features will be described shortly.

Product design Auction design Economic parameters

demand curve

Volume assessment

Market reference price Clearing algorithm Demand curve (y-axis) Demand curve (x-axis)

Strike price Price/bid caps T-1 reserved volume

Availability requirements De-rating factors

Availability penalties Investment levels

Minimum participation

threshold

Table 4. Elements determining the functioning of the CRM (Elia, 2019i).

38

4.1.1.1 Market reference price and strike price

The CRM proposed in Belgium is based on reliability options. A reliability option is a call

option and refers to the contract that includes the obligation of the capacity provider to

pay the option buyer the positive difference between of the market reference price 𝑝 and

the strike price 𝑠 for the capacity sold under the contract. In return, the capacity provider

receives a fixed premium fee. The conditions mentioned above apply during the contract

period of the call option and are independent whether the capacity provider is trading

electricity in the spot market. The purchaser of the reliability option, which is the TSO

Elia, pays the market reference price 𝑝 and receive the difference between 𝑝 − 𝑠 if 𝑝 >

𝑠. This means if 𝑝 > 𝑠, the net payment is 𝑠 and the maximum price for the purchaser is

capped at the strike price 𝑠. The reliability option allows buying at 𝑝, but when 𝑠 > 𝑝

the purchaser will buy rationally at the market reference price 𝑝. Capacity providers limit

the highest possible price from 𝑝 to 𝑠 by selling reliability options. In other words,

capacity providers change their fluctuating revenue to a fixed revenue, and therefore

reducing their income risk. Choosing for reliability options might be interesting for

capacity providers dealing with fluctuating revenue, e.g., peak plants. As mentioned

above, the payback obligation when 𝑝 > 𝑠 is independent whether the capacity provider

is trading electricity on the spot market. If the capacity provider cannot deliver the

demand required while not trading on the energy market, the payback obligation with

the reliability option is (𝑝 − 𝑠) (Vázquez, Rivier, & Pérez-Arriaga, 2002).

Figure 5 shows the development of the market reference price (here spot price) over time

and the imposed strike price. 𝑠 > 𝑝 is true in 𝑡1 and 𝑡5, whereas 𝑝 > 𝑠 is true in 𝑡2, 𝑡3

and 𝑡4. 𝑡3 Illustrates the period where the capacity provider fails to deliver the contracted

capacity (Vázquez, Rivier, & Pérez-Arriaga, 2002).

Figure 5. Application of reliability options (Vázquez, Rivier, & Pérez-Arriaga, 2002).

39

The different outcomes of the reliability mechanism for the buyer of capacity and the

capacity provider are indicated in Table 5.

Time T1 T2 T3 T4 T5

Demand pays (€/MWh) 𝑝 𝑝 − (𝑝 − 𝑠) = 𝑠 𝑠 𝑠 𝑝 Capacity provider receives (€/MWh)

Excluding reliability option 𝑝 𝑝 𝑝 𝑝

Capacity provider receives (€/MWh)

Including reliability option 𝑝 𝑝 − (𝑝 − 𝑠) = 𝑠 −(𝑝 − 𝑠) − 𝑝𝑒𝑛 𝑠 𝑝

Table 5. Possible outcomes reliability options (Vázquez, Rivier, & Pérez-Arriaga, 2002).

Reliability options should limit windfall profits and increase the availability incentive

during moments of (imminent) scarcity. Imminent scarcity moments and moments when

the market reference price 𝑝 exceeds the strike price 𝑠 are highly correlated. This

provides an incentive for capacity providers to be active in the energy market during

moments of (imminent) scarcity (Elia, 2019f).

The equation below describes the payback obligation to the TSO if the market reference

price exceeds the strike price. The payback obligation is equal to the market reference

price minus the strike price (𝑝 − 𝑠) times the number of hours 𝑡 to which 𝑝 > 𝑠 is true.

(Vázquez, Rivier, & Pérez-Arriaga, 2002).

𝐶𝑐𝑟𝑚 = ∫ (𝑝 − 𝑠) ∗ 𝐶 𝑑𝑡𝑝>𝑠

(8)

𝐶𝑐𝑟𝑚 Costs arising from participating in the CRM [€/𝑦𝑒𝑎𝑟];

𝑝 Market reference price [€/𝑀𝑊ℎ];

𝑠 Strike price [€/𝑀𝑊ℎ];

𝐶 Obligated capacity, i.e., installed capacity times the corresponding de-rating factor [𝑀𝑊].

40

According to Elia’s proposal for the Belgian CRM, the market reference price ‘represents

a continuous and relevant energy (spot) price signal (€/MWh) of the Belgian power

market revenue, and capturing moments relevant for the security of supply’ (Elia, 2019f,

p. 4). The Belgian day-ahead market price has been proposed as the market reference

price because of its liquidity. The price signal is closely related to the (near) scarcity

moments, and all technologies can react to the price. No alternatives have been proposed

during the CRM Task Force stakeholder meetings organised by Elia. Therefore, the

Belgian day-ahead market price will be considered as the market reference price in this

research (Elia, 2019g; Elia, 2019f).

One single strike price will be applied in Belgium according to the latest CRM design notes

(Elia, 2019m). The strike prices of European reliability options CRMs are taken into

consideration. Both strike prices of the Italian CRM and the Irish all-island CRM are taken

into consideration. Table 6 shows the strike price of the Italian CRM and the Irish all-

island CRM. In Italy, the strike price proposed is equal to the variable costs of the peak

technology on the energy market. In the latest proposal, it was the variable costs of an

Open Cycle Gas Turbines plant (OCGT), which was € 125/MWh (European Commission,

2018). The strike price in Irish all-island T-4 capacity auction 2022-2023 was

€ 500/MWh (EirGrid, & SONI, 2019c). Both strikes prices will be taken into account in

the scenarios.

CRM Strike price [€/MWh]

Italy 125

Irish all-island 500

Table 6: Mentioned strike prices in Italy and Irish all-island CRM (European Commission, 2018; EirGrid, & SONI, 2019).

41

4.1.1.2 Availability requirements and availability penalties

All awarded capacity providers in the CRM auction contribute to the adequacy in Belgium.

Elia monitors the availability of each capacity provider during ‘adequacy relevant

moments’ in the contracted period. The Availability Monitoring Trigger (AMT) to identity

these adequacy relevant moments, i.e., AMT moments, is the Belgian day-ahead market

price. The availability monitoring of the contracted capacity providers is triggered if the

Belgian day-ahead market price exceeds a yearly defined AMT price. Every hour the

availability monitoring is triggered is called an AMT hour, and consecutive AMT hours is

called an AMT moment (Elia, 2019k).

Storage, aggregation of demand response and emergency assets capacity providers are

energy-constrained, so situations occur in which the AMT moment lasts longer than the

capacity provider’s (energy) reservoir constraint. Therefore, their contribution towards

adequacy is determined in a Service Level Agreement (SLA). The SLA is based on the

capacity’s energy constraint, i.e., maximum storage depth. The capacity provider should

provide according to the SLA their capacity until the energy reservoir of its asset is

exhausted. Hereafter, there is no delivery obligation for the capacity provider anymore

for that day since the limit is one activation per day. Table 7 shows the different SLA

categories for energy-energy constrained capacity providers and corresponding energy

delivery duration. As an example, if the storage depth of an asset is one hour, e.g., EES

power and energy capacity dimensioning is 10 MW/10 MWh, it should deliver only

during one AMT hour per day the (contracted) obligated capacity (Elia, 2019k).

Category Duration [hour] Limit [activation/day]

SLA #1 1

1

SLA #2 2

SLA #3 3

SLA #4 4

SLA #5 8

SLA #6 ∞

Table 7. SLA categories for energy-constrained capacity providers. Adapted from Elia (2019k).

42

Differences between the capacity provider’s available capacity and the obligated capacity,

i.e., missing capacity, could occur because of planned or forced outages (Elia, 2019k).

Capacity providers should cover the additional capacity required in a secondary market.

Elected bids from capacity providers could be (partly) transferred to other prequalified

capacity providers of the capacity auction with excess capacity of the capacity auctions

for a certain period and price. Capacity providers are penalized if they cannot cover the

missing capacity in the secondary market (Elia, 2019j).

The secondary market is considered as a liquid market. It is assumed in this research that

the capacity required to meet the contracted availability requirement is always met by

capacity from the secondary market. Besides, the Belgian CRM does not exclude any form

of capacity in the auctions because of the European Commission’s technological

neutrality requirement. EES is even mentioned as an example in Elia’s CRM presentation

(Elia, 2019i). Therefore, the availability requirement to participate in the capacity

auctions will be most likely no issue for EES. Last, the expected high penalties provide a

financial incentive to comply with the capacity delivery obligation. Therefore, payback

obligation in terms of penalties is not taken into consideration.

4.1.1.3 Clearing algorithm

The capacity remuneration is a yearly fixed premium in euro per MW. The pricing

depends on the type of CRM design. In the pay-as-bid design, the capacity provider

receives the accepted bid price in the auction whereas in the pay-as-clear design all

capacity providers receive the same market-clearing price (Harbordm & Pagnozzi, 2014).

The pay-as-bid design will be applied to the first two CRM auctions, i.e., the T-4 auction

in 2021 and 2022, and the pay-as-clear design will be applied in both T-1 and T-4

subsequent CRM auctions.

43

4.1.1.4 Demand curve and price caps

The demand curve describes the volume, i.e., capacity, to be contracted as a function of

the price per MW per year. The curve is often defined by three points, each representing

the willingness to pay for a certain level of security of supply. One point on the curve

corresponds to the minimum capacity which should be contracted at a certain price cap,

i.e., maximum price bid. The second point represents the price and capacity required to

ensure the desired security of supply level. The last point represents the maximum

capacity which could be contracted at zero euro per MW. Elia computes the capacity

required each year based on different scenarios and sensitives. No further details have

been published yet regarding the demand curve (Elia, 2019l).

4.1.1.5 T-1 reserved volume

The volume required for the T-4 auction is based on the desired security of supply level.

The T-1 auction is used for the refinement of capacity supply and demand. The minimum

reserved volume is at least equal to the capacity that has, on average less than 200

operating hours per year to cover the total peak capacity (Belgische Kamer van

volksvertegenwoordigers, 2019a).

4.1.1.6 Minimum participation threshold

The minimum capacity participation threshold, after applying de-rating factors, under

which capacity providers cannot participate in the prequalification process and

therefore, in the capacity auction (Belgische Kamer van volksvertegenwoordigers,

2019a).

44

4.1.1.7 De-rating factors

The CRM should contract the capacity to provide a secure energy supply. However,

generation units, demand response providers and storage facilities are assumed not

always to be able to continuously deliver 100% of their of generation capacity due to

malfunctions, lack of energy capacity, maintenance or weather conditions. In the Belgian

CRM, the contribution of a technology or a unit within ‘’near scarcity’’ hours is reflected

by de-rating factors. Near scarcity is the situation where the national resources and

import could just meet the country’s electricity demand. Market price thresholds

determine the near scarcity hours (Elia, 2019e). The system is considered adequate when

the capacity can meet the load demand anytime, given a certain reliability standard (Elia,

2019k). The de-rated capacity participating in the CRM auction, which is the contribution

to adequacy, is equal to the nominal capacity times the relevant de-rating factor for the

capacity provider.

Each SLA category as presented in Table 7 will correspond to a specific de-rating factor.

The de-rating factor represents the contribution of an asset in the SLA category to the

adequacy in Belgium. No concrete de-rating factors have been proposed in Elia’s CRM

meetings yet. However, the first SLA category is 1-hour duration (Elia, 2019e). Thus, the

minimum storage depth of the BESS should be at least 1 hour to be rewarded in the

Belgian CRM.

45

The de-rating factors of the upcoming Italian reliability options mechanism have not been

published yet. Table 8 shows the de-rating factors of storage (excluding hydro storage)

in the T-4 capacity auction delivery year 2022-2023 in the United Kingdom’s CRM and

the Irish all-island’s CRM. The de-rating factor is dependent on the maximum storage

duration of the asset, which is the ratio power capacity/energy capacity. The greater the

storage duration, i.e., the relatively larger the energy capacity compared the power

capacity, the more capacity is awarded in the capacity auction. Table 8 also includes the

de-rating factor of the Polish capacity auctions in 2018 for the delivery year 2021-2022,

2022-2023 and 2023-2024. Striking is the minimum duration of storage in Poland of 4

hours.

CRM De-rating factor 1h

storage depth [%]

De-rating factor 2h

storage depth [%]

De-rating factor 4h

storage depth [%]

United Kingdom 29,4% 56,68% 80%

Irish all-island 39,4% 59,9% 70,2%

Poland n/a n/a 96,11%

Table 8. United Kingdom and Irish all-island De-rating factors T-4 capacity auction (deliver year 2022/23) and Poland de-

rating factors capacity auction (delivery year 2021-2022, 2022-2023, 2023-2024) (Rozporządzenie Minstra Energii; 2018;

EirGrid., & SONI, 2019; National Grid, 2018c).

46

4.1.1.8 Investment levels

The standard CRM contract is for a delivery period of one year. Depending on the

investment costs, both Belgian and foreign capacity providers may be eligible to a

maximum of 3, 8 or 15-year CRM contracts. Investments thresholds distinguish the

different contract periods and thus the capacity remuneration. The objective of the

different investment levels is to create a level playing field among potential capacity

providers with different investment costs (CREG, 2019).

The CREG has mapped the investment thresholds of European CRMs and presented in

Table 9 It is striking that the investment threshold for a delivery period of 15 years differs

greatly per country (186.000 – 705.882 €/MW). The CRM contract duration relevant for

this research will be explained in 5.5

CRM Delivery period [years] Investment threshold [€/MW]

United Kingdom 1 n/a

3 (T-4 auction 2018) 152.550

15 (T-4 auction 2018) 305.100

Irish all-island 1 n/a

Max. 10 300.000

Poland 1 n/a

5 (7 If C02 < 450kg/MWh) 117.647

15 (17 If C02 < 450kg/MWh) 705.882

Italy 1 n/a

15 186.000

Table 9. Investment levels of European CRMs (CREG, 2019).

47

4.1.2 Conclusions

The availability requirements and the availability penalties are relevant parameters to

consider since strict requirements and penalties can be a barrier to entry to the capacity

auction. However, both do not seem to cause any problems regarding participation in the

CRM auctions. Given the research objective, the clearing algorithm, demand curve, price

caps, and the T-1 reserved volume are beyond the scope of this research and not taken

into consideration. The market reference price and strike price are the fundamentals of a

CRM based on reliability options. The potential costs arising from the payback obligation

might have a significant impact on the competitiveness of EES. Besides, the de-rating

factor directly influences the capacity remuneration of EES and therefore the competitive.

The investment levels are related to the number of capacity delivery periods and thus the

period for which the capacity provider receives remuneration.

To conclude, the most important CRM auction parameters in this research for evaluating

the competitiveness of BES are the reference price, strike price and the de-rating factors.

48

4.2 Appropriate EES technologies

Much research has been conducted into large-scale electrical storage. Studies from CREG

(2015) and FOD Economy (2015) and literature on EES discussed in 2.3 could provide

insights into the different EES technologies. The technologies taken into consideration

are Large-scale Pumped Hydroelectric Energy Storage (PHES), Compressed Air Energy

Storage (CAES), Flywheel Energy Storage (FES) and Battery Energy Storage (BES).

PHES and CAES are mainly used for energy time-shifting because of the low energy costs,

low self-discharge rates and the ability to charge and discharge economically optimally

over many hours (up to 40 hours is not exceptional) (IRENA, 2017). However, PHES and

CAES deal with geographic restrictions because PHES needs significant differences in

height between two reservoirs and CAES needs underground cavern reservoirs to store

compressed air. Both requirements cannot be met in Belgium due to lack of space for

PHES and no feasible geographical resources for CAES (Limpens, & Jeanmart, 2018).

FES has several benefits such as allowing a high number of charge-discharge cycles,

providing high power capacity and fast response time (Pena-Alzola, Sebastián, Quesada,

& Colmenar, 2011). However, the energy installation costs of FES is significantly higher

compared to PHSES, CAES and Li-ion batteries (respectively 140, 56 and 7 times higher

in 2016 and still is towards 2030). Also, the technology deals with self-discharge rates of

15% per hour and therefore less suitable for energy time-shifting (IRENA, 2017).

49

Lithium-ion (Li-ion) storage, which is a type of Battery Energy Storage (BES), is an

emerging technology in the utility-scale energy market and has several advantages over

other technologies. Major technical benefits of Li-ion batteries are the short response

time, high power and energy density, and high round-trip efficiency (Luo, Wang, Dooner,

& Clarke, 2015). From an economic perspective, the price of an average Li-ion battery

pack dropped 85% from 2010 to 2018. According to the historical battery pack prices

between 2010 and 2018, BloombergNEF (2019a) computed a learning curve of 18%. This

means every doubling of the cumulative volume of Lithium-ion battery packs decreases

the price by 18% and results in an average Li-ion battery pack price is $ 62/kWh in 2030

(BloombergNEF, 2019a). The Li-ion battery pack price outlook until 2030 is shown in

Figure 6.

Figure 6. Price development of Li-ion battery pack price outlook till 2030 (BloombergNEF, 2019a).

However, battery price forecasts should be interpreted with caution. BloombergNEF

(2019b) predicted four months after the price forecasts in Figure 6 an average price of

$ 88/kWh in 2030 (~ 40% difference in price prediction four months). In addition to the

strong price reduction, Li-ion storage is often preferred for fast frequency related

ancillary services because of its fast response time.

50

Eneco has also investigated the most suitable EES technologies for less than three hours

of storage duration applications in 2019. The research concludes that less than three

hours storage duration with Li-ion batteries, focussing on the FCR, imbalance, and

intraday market, might be the most economically feasible (Eneco, personal information,

May 2, 2019).

Based on research from IRENA (2017), BloombergNEF (2019a) and Eneco (personal

information, May 2, 2019), the focus of this research will be specifically on Battery Energy

Storage (BES), in particular, Li-ion storage. Li-ion storage will hereafter be referred to as

BES or Battery Energy Storage System (BESS) when the entire system is described. The

chemistry choice for the Li-on BES is nickel-manganese-cobalt (NMC) because of the

favourable combination of characteristics such as performance, safety, and costs (IRENA,

2017). NMC Li-ion cells are used as well in Eneco’s 48 MW BES system in Jardelund

Germany and provide frequency control reserve to maintain the network frequency

(Eneco, 2018; Eneco, personal information, August 2, 2019).

4.2.1. Common BES projects

There are several BESS active on energy markets, varying in power capacity and energy

capacity. In the United Kingdom, three kinds of BES projects trends, differing in power

capacity size and application, have been identified over the past years and is coming up

(Energy Storage News, 2019). The first category includes utility-scale stand-alone 50 MW

BES projects with long duration capacity contracts and income derived from energy

markets. Second, 10-20 MW BES projects in combination with (renewable) generation

assets such as wind and solar. Last, smaller-scale projects active in the Commercial &

Industrial (C&I) sector also in combination with generation assets.

51

4.3 Opportunities in the electricity markets

This section describes the opportunities for BES in the Belgian electricity market. First,

the Belgian wholesale electricity markets and the ancillary services offered by Elia will be

explained shortly in the description section. Subsequently, the technical feasibility and

the revenue potential of BES in the electricity market will be considered in the analysis

section.

4.3.1 Wholesale electricity market

4.3.1.1 Description

The EPEX SPOT (formerly Belpex Spot Market) is the short term Belgian power exchange

and operates in three market segments: the Day-Ahead Market (DAM), Continuous

Intraday Market (CIM) and the Strategic Reserve Market Segment (SRM).

The DAM is the auction for daily trading of electricity the following day. Buyers and sellers

submit their volume and price hour by hour, and afterwards, the supply and demand

determine the hourly day-ahead price (EPEX SPOT, n.d. –b).

The CIM is the auction for continuous daily trading of electricity the same day. Trading of

hourly products and several hours is possible from the day before delivery at 15:00 till

five minutes before delivery (EPEX SPOT, n.d. –c). This implies the possibility to trade at

15:00 for the next day 23:00-00:00, which is 33 hours in advance. Greater price volatility

is expected at the opening of the intraday trading slot because there is less certainty

regarding weather conditions, what other parties are trading and other market

circumstances. For example, due to fewer hours of sunshine or the wind blows less than

was expected. Traders have better information about their position and prices closer to

the delivery moment. Therefore, trades in the intraday market are done, on average, 5

minutes up to 8 hours ahead of the delivery moment (Eneco, personal information, July

4, 2019).

52

The SRM is introduced for guaranteeing the security of supply during the winter period.

Elia is responsible for the design, auction, and operation of the strategic reserve (EPEX

SPOT, n.d. –d). The TSO instructs contracted capacity providers during shortages to

increase their load or instructs consumers to reduce their load during an activation of the

strategic reserve (Elia, 2019n). Table 10 shows the main characteristics to distinguish

the three Belgian market segments of the EPEX SPOT Belgium.

DAM CIM SRM

Instrument

series

1-hour blocks 1-hour blocks3 1-hour blocks

Listed for Order

submission

14 trading days

before delivery

until noon

trading day

before delivery

From 15:00 trading day

before delivery until 5

minutes before delivery

The day before delivery for Strategic

Reserve Participant; Direct

Participants will have neither the

possibility to submit Orders on the

Belpex SRM via a different

mechanism nor the possibility to

cancel or modify such replicated

Orders

Trading days Every calendar

day

Every calendar day Every calendar day during the winter

period

Fixing Process Auction Continuous trading Strategic Reserve Allocation

Trading hours Order book

closing at 12:00

24/7 Order book closing at the publication

of DAM results

Table 10. Specification of the three EPEX SPOT Belgium market segments (EPEX SPOT, n.d. -e).

3 Standard is 1-hour blocks but the blocks are freely definable (EPEX SPOT, n.d. –c).

53

4.3.1.2 Analysis

Figure 7 shows the Belgian day-ahead and intraday (BELPEX) distribution of the market

price difference between consecutive hours for a half year between December 4, 2018,

and June 4, 2019 (Eneco, personal information, June 26, 2019).

Figure 7. Sorted (high-low) difference in Belgian intraday and day-ahead prices of consecutive hours for a half year

between December 4, 2018, and June 4, 2019. (Eneco, personal information, June 26, 2019).

Figure 8 focuses on the hours that occur less than 100 hours in that particular half-year.

For example, during less than 10 hours this specific half-year, the price difference

between consecutive hours is around € 25/MWh in the day-ahead market and almost

€ 40/MWh in the intraday market. The blue peak at the beginning of Figure 8 indicates

greater market price volatility in the intraday market compared to the day-ahead market.

EES generate income by capturing price spread in the electricity market and therefore

benefits from price volatility, i.e., relative differences in market prices. This means EES’

potential revenue in the Belgian intraday market is greater than the potential revenue on

the Belgian day-ahead market. Therefore, the Belgian intraday market is preferred from

an economic perspective and considered in this research.

Figure 8. Sorted (high-low) difference in Belgian intraday and day-ahead prices of consecutive hours for a half year

between December 4, 2018, and June 4, 2019, < 100 hours. (Eneco, personal information, June 26, 2019).

-100

-50

0

50

100

Mar

ket

pri

ce

dif

fere

nce

co

nst

ruct

ive

ho

urs

[€

/MW

h]

Duration [h]Intraday market Day-ahead market

0

20

40

60

80

100

120

1 10 19 28 37 46 55 64 73 82 91 100

Mar

ket

pri

ce d

iffe

ren

ce

con

stru

ctiv

e h

ou

rs [

€/M

Wh

]

Duration [h]Intraday market Day-ahead market

54

4.3.2 Ancillary services

4.3.2.1 Description

The electrical consumption must be continuously and instantaneously matched to the

electricity generation. Disruptions cause deviations in the system frequency and affect

the reliability of the electricity supply (Oudalov, Chartouni & Ohler, 2007). As a TSO, Elia

has the responsibility to evaluate and determine yearly the volume required to maintain

the reliability and of the Belgian grid (Elia, n.d. –c). Elia purchases flexible power capacity

and reserves from consumers and generators connected to the grid as it does not have

assets to maintain the frequency of the power grid. These services provided by

consumers and generators are called ancillary services (Elia, n.d. –b).

At the moment, the European electricity market is evolving towards an integrated market

consisting of various European TSOs (ENTSO-E, n.d. -a). The European guideline on

electricity balancing (EBGL) has been in force in the EU since November 2017 and should

increase competition, ensure the security of supply, transparency, and integration of

balancing markets (ENTSO-E, 2018). Two main elements of the guideline which influence

the design of the Belgian ancillary services are the notation of technological neutrality,

which must lead to more technologies being able to offer ancillary services, and the short-

term procurements (European Commission, 2017b). Moving from weekly towards daily

procurement should increase market liquidity and efficiency (Elia, 2018b). Elia’s long

term vision is to create harmonised, neutral rules for the ancillary services Frequency

Containment Reserve (FCR), automatic Frequency Restoration Reserves (aFRR) and

manual Frequency Restoration Reserves (mFRR) (Elia, 2019c). Deployment of multiple

ancillary services by one Balancing Service Provider (BSP) is possible under certain

conditions (Elia, 2018a; Elia, 2019b; Elia, 2019c).

55

The current ancillary services in 2019 no longer exist in the first delivery year of the first

CRM auction scheduled in 2025. Therefore, the upcoming ancillary products in 2025 will

be discussed, as far as known from publications from the European Network of

Transmission System Operators for Electricity (ENTSO-E) and Elia. Table 11 provides an

overview of the considered ancillary services, including the previous terminology, main

product characteristics, and the expected date of the first procurement of the final

products. Final products mean that there are currently temporary forms of the upcoming

balancing products.

New terminology Previous terminology Activation

frequency

Activation

energy

Procurement

Frequency Containment

Reserve (FCR) Primary reserve (R1) +++ + July 2020

Automatic Frequency

Restoration Reserves (aFRR) Secondary reserve (R2) +++ +++ July 2020

Manual Frequency Restoration

Reserves (mFRR) Tertiary reserve (R3) + +++ February 2020

Table 11. Overview of the (future) ancillary services (Elia, 2017a; Elia 2018d; Elia, 2019b).

One of the main implications of the European balancing guideline EBGL is moving to daily

procurements for all three ancillary services. There should be auctions for six

independent 4-hours blocks in a day for these three Belgian ancillary services. Elia will

allow 24-hours block for aFRR providers because it fits better the technical specifications

of the participating technologies. (Elia, 2018a; Elia, 2019b; Elia, 2019c). The sequence of

the daily procurement cycle will be FCR, mFRR and mFRR. The different ancillary services

will be discussed shortly in the following subchapters.

56

4.3.2.1.1 Frequency Containment Reserve

Frequency containment reserve (FCR, former ‘primary reserves’), focusses on balancing

the high-voltage European interconnected system. Any frequency deviations

automatically activate contracted primary reserves set by the European Network of

Transmission System Operators for Electricity (ENTSO-E) (Elia, n.d. –a). FCR should

“stabilize the system frequency at a stationary value after a disturbance or incident in the

time-frame of seconds, but without restoring the system frequency and the power

exchanges to their reference values” (ENTSO-E, 2009, p. 12). FCR product suppliers must

reserve a capacity band (upwards and downwards) equivalent to the contracted capacity

in the delivery period.

The total capacity required will be procured daily on a regional platform Regelleistung

with TSO’s from Germany, Austria, the Netherlands, France, and Denmark (Regelleistung,

n.d. -a). The product in the auction is the FCR symmetric 200 mHz service. The provider

should deliver maximum FCR power capacity between -200 mHz and +200 mHz

deviation from the nominal frequency of 50 Hz and the reaction should be proportionally

between -200 mHz till -10 mHz and +10 mHz till + 200 mHz (Elia, 2019b).

57

4.3.2.1.2 Automatic Frequency Restoration Reserve

The automatic Frequency Restoration Reserve (aFRR, former ‘secondary reserves’) takes

over the responsibilities of FCR. The generation assets are activated automatically in less

than 15 minutes after an imbalance incident occurs (ENTSO-E, 2009). The aFFR product

is characterised by the high frequency of activations and a large amount of activated

balancing capacity, as indicated in Table 11. It is considered as the most complex

balancing product offered (Elia, 2018a). The aFRR provider determines a baseline

(reference power), which is the power that the aFFR provider (or aggregation of

providers) would have consumed or added to the grid at a certain delivery point without

providing the aFRR service. The aFRR provider sends the value to Elia every four seconds.

The difference between the baseline and the measured power at the delivery point is the

delivered energy by aFRR provider (Elia, 2018a).

According to Elia’s aFRR design note, aFRR providers should place bids in both directions.

This means, aFRR providers are obligated to bid both symmetrical, i.e., volume upwards

and volume downwards, and asymmetrical, i.e., upwards or downwards (Elia, 2018a).

4.3.2.1.3 Manual Frequency Restoration Reserves

The manual Frequency Restoration Reserves (mFRR, former “tertiary reserves”) replaces

and can be activated as a supplement to aFRR. MFRR is manually controlled by Elia and

is normally activated for a longer period compared to FCR and aFRR (ENTSO-E, 2009).

58

4.3.2.2 Analysis

Table 12 summarises balancing processes availability requirements and the activation

characteristics from Table 11. As mentioned in chapter 5, Li-ion batteries are

characterized by their short response time, high energy density, and high round-trip

efficiency (Luo, Wang, Dooner, & Clarke, 2015). Therefore, it is technically possible to

develop a Li-ion BESS which meets all the balancing process requirements regarding

reaction time, activation time, and the minimum time sufficient energy for full activation.

Balancing process Maximum

full reaction

time

Maximum

time to full

activation

Minimum time

sufficient energy for

full activation

Activation

frequency

Activation

capacity

Frequency

Containment

Reserve (FCR)

Not

artificially

delayed and

– as soon as

possible

15 seconds

(50%), 30

seconds

(100%)

15 minutes (recovery

of energy capacity

within 2 hours after

the end of the alert

state)

+++ +

Automatic

Frequency

Restoration

Reserves (aFRR)

30 seconds 7,5 minutes 4 hours (i.e., one 4-

hours block)

+++ +++

Manual Frequency

Restoration

Reserves (mFRR)

15 minutes 4 hours (i.e., one 4-

hours block) + +++

Table 12. Technical characteristics ancillary services (Elia, 2017a; Elia, 2018a; Elia, 2019b; Elia, 2019c).

59

AFRR and mFRR require capacity which can deliver four hours of energy continuously.

This means a BESS should have a storage depth of at least four hours. The minimum

energy delivery period of the FCR 2020 procurement has not been determined by

European Network of Transmission System Operators for Electricity (ENTSO-E). The

minimum activation time will be at least 15 and not greater than 30 minutes during the

alert state (Elia, 2019b). The alert state is triggered by a continuous frequency deviation

is greater than 100 mHz during 5 minutes or 50 mHz during 15 minutes. (European

Commission, 2017c). Besides, an additional 10 minutes is required during normal state

for energy-constrained capacity providers, i.e., BES, in Belgium (Elia, 2019b). In the

normal state, the capacity provider is not violating the European frequency deviations

rules (European Commission, 2017c). All European TSOs should assess the required

minimum activation time during alert state and hand in their proposal in April 2020

(ENTSO-E, n.d. -b). At the moment, a minimum duration of 15 plus 10 minutes or 30 plus

10 minutes is equally assumable since no preference is given in Elia’s proposal. Therefore,

this research assumes the ‘worst-case scenario’ of a mandatory energy delivery

availability requirement of 40 minutes.

The average price of selected bids for the considered ancillary services between January

2019 and August 2019 is shown in Table 13 (Elia, n.d. –c). FCR symmetrical 200 mHz

(common auction) is the new FCR product and since July 2019 procured daily on the

regional platform ‘Regelleistrung’ (Regelleistung, n.d. -a). The weekly procurement of

FCR on Elia’s local (Belgium) platform will be fully procured daily on Regelleistung from

July 2020. (Elia, 2019b). The participating countries on the regional platform are Austria,

Belgium, France, Germany, Netherlands, and Switzerland (Regelleistung, n.d. -a; Elia,

2019b).

Table 13 shows that the average price for FCR symmetrical 200 mHz product in the

regional auction is 15% lower compared to the FCR symmetrical 200 mHz product in the

Belgian auction. The expectation is that the prices for R2 and R3 will also drop if R2 is

auctioned on the regional platform as aFRR and R3 is auctioned as mFRR. The prices will

be pressed because of the increasing volume of the joint tenders and the liquidity of the

balancing market increases. Second, because of the daily procurement in the sequence

FCR-aFRR-mFRR, providers try to participate in the different auctions.

60

This is especially true for the mFRR which the auction was first on a monthly basis. In the

future, ancillary service providers can make better forecasts in the day-ahead compared

to month-ahead regarding the (technical) availability, which translates into an increase

of competing volume. This leads to a lower price because the premium on risk is higher

in the monthly prices from uncertainty, not selling it (Elia, 2019b).

The average R3 prices (flex and standard) are already lower than the average FCR

common auction price, and the average R2 price (upward/downward) might decrease to

the same price as the average FCR common auction price.

Previous terminology New terminology Auction Average price

[€/MW/h]

R1 upward Frequency Containment Reserve (FCR) Belgium 2,04

R1 downward Frequency Containment Reserve (FCR) Belgium 1,72

R1 symmetrical 100 mHz Frequency Containment Reserve (FCR) Belgium 13,45

FCR symmetrical 200mHz n/a Common 8,84

FCR symmetrical 200mHz n/a Belgium 10,43

R2 upward/downward Secondary reserve (R2) Belgium 12,61

R3 Flex Tertiary reserve (R3) Belgium 4,16

R3 Standard Tertiary reserve (R3) Belgium 7,07

Table 13. Average price of selected bids for the ancillary services between January 2019 – week 31 August 2019 (Elia, n.d.

–c).

Comparing one hour of storage depth for FCR with R2/aFRR or R3/mFRR, which requires

a minimum of four hours of storage depth, the storage costs become three times as high,

given the storage costs calculations in the next paragraph. However, the aFRR or mFRR

revenues are not expected to be four times as high. Last, the activation frequency of

R2/aFRR is higher compared to FCR as mentioned in Table 13. Thus, the higher storage

costs do not outweigh any potential additional revenue. The economic concept is called

‘diminishing returns’.

Therefore, this research only evaluates the ancillary service FCR symmetrical 200 mHz

(common auction) and the Belgium intraday market as described in 4.3.1.

61

4.4 Price developments

The two sources of income of BES in this research are the intraday market and the

ancillary service FCR market, as discussed in the previous section. This section discusses

the future price developments of both markets.

4.4.1 Intraday market

No research has been found on the Belgian intraday market price developments.

However, Elia has published in their adequacy and flexibility study average day-ahead

market price forecasts in Belgium for 2020 till 2030. The price forecasts are shown in

Figure 9. The model used in this study assumes all the energy is sold in the day-ahead

market and the electricity price is equal to the hourly marginal price, which is based on

the variable costs of the energy production facilities (Elia, 2019d). Two CO2 price

scenarios of the International Energy Agency (IEA) World Energy Outlook 2018 are taken

into account (IEA, 2018). The New Policies Scenario (ref CO2 in Figure 9) is considered as

IEA’s baseline scenario and takes in short countries’ CO2 emissions reduction plans and

commitments into account. The scenario assumes the CO2 price is about € 30 per tonne

in 2030, which is similar to results from BNEF, HIS Markit and EU reference Scenario. The

Sustainable Development Scenario (high CO2 in Figure 9) represents, in short, the

pathway to meet the Paris Agreement climate goals and the scenarios assumes a higher

CO2 price compared to the New Policies Scenario of around € 80 per tonne in 2030 (Elia,

2019d; IEA, 2018). The ‘decentral’ scenario assumes a capacity mix consisting of low

CAPEX/high CAPEX technologies (e.g., emergency generators for load shedding, diesel

generators). The ‘efficient gas’ scenario assumes a capacity mix consisting of new built

Combined Cycle Gas Turbine (CCGT) plants and Combined Heat and Power (CHP) plants.

Elia predicts gas-fired power plants will set the day-ahead market price to meet the

energy demand in the future. The CO2 price and the gas price are the main drivers of the

marginal costs of gas-fired power plants, so therefore these prices have a major influence

on the day-ahead market price forecasts (Elia, 2019d).

62

Figure 9. Average day-ahead market price forecasts in Belgium 2020-2030 under different CO2 and technologies.

4.4.1.1 Relation between day-ahead and intraday market price

Based on multiple historical hourly intraday market prices, the Volume Weighted

Average Price (VWAP) is calculated per hour. The VWAP is the weighted average price

what has been paid for one MWh for a specific delivery hour. Thus, the VWAP intraday

market price is close to the imbalance price if mostly all intraday market trades are made

before the delivery hour. The VWAP intraday market price is determined for the random

month, e.g., March in 2019. The price development has been plotted together with the

Belgium day-ahead market price in figure 10. What emerges clearly from figure 10 is the

same movement of the intraday price level and the day-ahead price level in the

considered period. No clear trend in the intraday market price can be identified on a

monthly timescale.

Figure 10. Hourly VWAP Belgium intraday and day-ahead market prices March 2019.

-20

0

20

40

60

80

100

Ho

url

y m

ark

et p

rice

[€

/MW

h]

01-Mar-2019 00:00 - 31-Mar-2019 23:00 [hours]

Intraday market Day-ahead market

63

The VWAP intraday market price has also been determined on a monthly scale from

January 2014 – July 2019 in Figure 11. The same movement of the Belgian intraday

market and the day-ahead market is observable on a monthly level over a longer period,

Figure 11. Hourly VWAP Belgium intraday and day-ahead market prices (per month) January 2014 - July 2019.

Based on the same movement of day-ahead and intraday market prices, as shown in

figure 10 and Figure 11, Elia’s day-ahead market price forecasts for Belgium 2020-2030

will be used as a proxy for the future intraday market price developments.

The market prices corresponding with the New Policies Scenario (ref CO2) will be used

since this the most broadly accepted scenario by researchers and policy advisors. The

average price of the ‘decentral’ and ‘efficient gas’ scenario are close together, and the

average of both scenarios is taken into account.

0

10

20

30

40

50

60

70

80

90

Jan

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Ap

r-1

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19

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nth

ly m

ark

et p

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[€

/MW

h]

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Intraday market Day-ahead market

64

4.4.2 FCR

There are no FCR price forecasts published by research agencies, the Belgian government

or Elia. In essence, the demand, and therefore the costs and the price, of ancillary services

increase by higher penetration of renewables because of its intermitted supply (Chuang,

& Schwaegerl, 2009). On the other hand, the procurement of FCR and aFRR will be

separated in July 2020. This means the interdependence of FCR and aFRR decrease and

Elia expects an increase of bids from new entrants in the FCR market (Elia, 2018c). Also,

because of the decrease in battery prices, as mentioned in 5.23, the number of new BESS

projects participating in ancillary services auction is likely to increase. Also, aggregators,

which aggregates small residential batteries to a large virtual battery, see ancillary

services as an interesting revenue stream. For example, 90.000 BESS representing 500

MWh, which is two-thirds of the total operational BESS energy capacity online in

Germany (Apricum, 2018). The rise of residential batteries may also spread to Belgium

and other neighbouring countries. Elia expects the volume of the ancillary services will

not significantly change in the future. Besides, more flexible generation capacity will be

sourced abroad because of the further integration of the European ancillary service

markets as explained in 4.3.2.1. Last, the nuclear power plants, which supplies a major

part of the current Belgian power baseload, will be replaced by (to some extent) flexible

generation capacity. This could lead to increased competitiveness in the FCR market and

lower prices (Elia, 2019d).

65

5. Model description

This chapter first introduces a conceptual model of the research, which outlines all

aspects that are important in determining the competitiveness of BES. Subsequently, the

power and energy capacity dimensioning of the BESS will be discussed. Thereafter, the

relevant approaches for modelling the FCR and the intraday market income will be taken

into account. At the end of this chapter the scenarios and eventually, the experiment

design will be presented.

5.1 Conceptual model

The competitiveness will be evaluated according to the constructed competitiveness

evaluation model which has been developed for this research and presented in Figure 12.

The conceptual model is based on the description of the central buyer mechanism

reliability options model from PwC (2018) and the definition of competitiveness from

chapter 2.4. The model aims to capture all the relevant elements that are important to

consider for the decision-making process regarding participating in the CRM capacity

auctions.

Figure 12. Competitiveness evaluation model.

66

5.2 BESS setup

5.2.1 Dimensioning

The initial situation in this research is a 10 MW BESS, which is a typical power capacity

dimension used in the past by Irish all-island and United Kingdom’s capacity auctions

(CEPA, 2019; National Grid, 2018c; National Grid, 2019).

The appropriate energy capacity (MWh) for a specific power capacity depends on the

application. However, according to the proposed SLA categories in 4.1.1.2, the minimium

storage duration to obtain a CRM contract is 1 hour. Therefore, the minimum storage

depth of the BESS would be 1 hour, so the BESS dimension would be atleast

10 MW/10MWh.

The specific power and energy capacity dimension taken into consideration in this

research will be discussed for a BESS operating each in the intraday and FCR market

5.2.1.1 Intraday market

Income in the intraday market is generated from energy time-shifting. For energy time-

shifting, as introduced in 2.3, a greater storage depth is preferred to make optimal use of

price spreads. The chosen storage depth for the intraday market are 1 hour and 2 hours.

1-hour BESS is initial setup for determining the competitiveness of BES. Although the 2-

hour BESS is expected to generate more income in the intraday market, the additional

income might not outweight the additional storage costs related to the doubling of the

storage depth, i.e., energy capacity. The forecasted CAPEX in 2025 would namely increase

by 75% if the storage depth is doubled and the income is not likely to increase with this

percentage (IRENA, 2017). More experiments will be made if a greater storage depth than

1 hour is economically profitable.

67

5.2.1.2 FCR market

The minimum BESS dimension is 10 MW/10 MWh. In this research the equivalent energy

constraint for energy-constrained capacity providers, e.g., BES, operating in the FCR

market should be at least 40 minutes as explained in 4.3.2. Given the 10 MW power

capacity and the minimum 40 minutes storage duration, the required energy capacity is

6,7 MWh. In addition, energy-constrained capacity providers should increase their power

capacity dimensioning by 25% to ensure continuous FCR activation (ENTSO-E, 2018a).

Thus, the dimensioning of BESS operating in the FCR market should be at least 12,5

MW/6,7 MWh in order to comply with the FCR requirements. This means, 3,3 MWh is

unused by the BESS. In the second BESS setup, the 3,3 MWh will be used for energy time-

shifting in the intraday market.

The four BESS setups taken into consideration in the research are shown in Table 14.

Power capacity

[𝑴𝑾]

Energy capacity

[𝑴𝑾𝒉]

Hours of storage depth

[𝒉]

Application

10 10 1 Intraday market 10 20 2 Intraday market 12,5 10 1 FCR

12,5 10 1 FCR + intraday market Table 14. BESS setups taken initially into consideration.

68

5.2.1 Costs and technical parameters

Table 15 shows the assumptions on the Li-ion storage costs parameters and technical

parameters for the years 2020, 2025 and 2030. The CAPEX, the maximum lifetime and

the maximum number of cycles are based on a public report from the International

Renewable Energy Agency (IRENA) (IRENA, 2017). The definition of a full cycle is further

explained in appendix 12.1. The CAPEX energy and power installation costs involve all

the costs of the BESS, e.g., battery pack, thermal management, controls, installation,

converter, etc. (IRENA, personal communication, October 9, 2019). The percentage O&M

costs, roundtrip efficiency, depth of discharge and the required rate of return are based

on assumptions of Eneco (Eneco, personal information, May 21, 2019; Eneco, personal

information, August 2, 2019). The Belgium inflation and euro/dollar exchange rate are

based on the current rate of August 2019. The corporate tax rate is the rate in Belgium

from 2020 (XE, 2019; Statbel 2019; KBC, 2018).

Parameter 2020 2025 2030

Effective CAPEX energy installation costs4 [€/kWh] 326 235 169

CAPEX power installation costs [€/kW] 108 77 55

Percentage O&M costs of total CAPEX [%] 3 3 3

Roundtrip efficiency [%] 93 93 93

Depth of discharge [%] 90 90 90

Maximum full cycles [#] 2406 3031 3819

Maximum cycle life [year] 14 16 18

EUR/USD exchange rate [€/$] 1,10 1,10 1,10

Required rate of return [%] 7,00 7,00 7,00

Inflation on O&M and commodity [%] 1,26 1,26 1,26

Belgium corporate tax [%] 25 25 25

Table 15. Li-ion BESS costs parameters and values (IRENA, 2017; Eneco, personal information, May 21, 2019; Eneco,

personal information, August 2, 2019; XE, 2019; Statbel, 2019; KBC, 2018).

4 (nominal) CAPEX divided by the maximum depth of discharge of 90%

69

5.3 Intraday market income modelling

5.3.1 Description

As mentioned in 4.3.1, EES generate income by capturing price spreads in the intraday

market. The operation is called ‘electric time-shifting’ and includes purchasing (charging)

of electric energy when demand and energy prices are low and selling (discharging) the

energy when demand and energy prices are high. (Energy Storage Association, n.d.-a). As

explained in 3.3.2, the expected wholesale market income, i.e., intraday market income,

is computed with the use of Linny-R. This software tool determines the most appropriate

operation schedule of BES in order to maximize profit.

Figure 13 presents the visualisation of the Linny-R model for calculating the intraday

market income. The different elements in the figure below can be categorized into

products processes and links. The products ‘Input BE ID price 2021’ and ‘Output BE ID

price 2021’ consists of the Belgium intraday market price forecasts of 2021 (8760 hours

thus 8760 prices). The processes ‘Loading battery process’ and ‘Discharging battery

process’ take care of the charging and discharging process whereby the battery can be

charged and discharged each hour. The arrow from ‘Input BE ID price 2021’ to ‘Loading

battery process’ with the value 1,08 represents the BESS roundtrip efficiency of 93%. This

implies the price spread must overcome the round trip efficiency related to the charging

and discharging process. The product ‘Li-ion storage’ represents the storage reservoir of

the battery with a maximum storage depth of 1 hour. The storage reservoir is either full

(1-hour storage) or empty (0-hour storage) in this example. Finally, the product ‘Cycle

cost’ represents the fictive costs associated with the charging and discharging process

and the value is equal to or smaller than zero. The product aims to reduce the number of

cycles, which will be further explained in the next subchapter.

Figure 13. Linny-R model for calculating the intraday market income.

70

5.3.1.1 Roll and look-ahead period

As mentioned in 4.3.1.1, trades in the intraday market are done, on average, 5 minutes up

to 8 hours ahead of the delivery moment. Even though you can look further ahead in

practice (up to 33 hours), there has been chosen for a more conservative forecast, which

the look-ahead period is constrained to 8 hours in this research and the roll period is 1

hour. Basing revenue forecasts on historical prices implies perfect foresight under the

assumption of knowledge on the realized prices and, which will further be discussed in

8.2.2.2.

71

5.4 FCR income modelling

The remuneration of electric time-shifting in the intraday market is per energy unit

(€/MWh). In contrast, FCR providers receive remuneration for the reservation of power

capacity (€/MW). The results of the current FCR symmetrical 200 mHz product in the

common auction on Regelleistung between 1 July 1, 2019, and September 29, 2019, are

shown in Figure 14. It shows the Belgium marginal capacity price, which represents the

price of the last bid to meet the capacity demand. Every awarded Belgium capacity

provider receives the same remuneration from Elia (ENTSO-E, 2018b).

Figure 14. Marginal capacity price in Belgium on the regional platform between July 1, 2019 – September 29, 2019

(Regelleistung, n.d. -b).

The results shown in Figure 14 has let to the conclusion that no clear trend upwards or

downwards can be identified. The average price during the period was €

213,55/MW/day, the minimum price € 140,33/MW/day, and the maximum price €

447,34/MW/day. Thus, if the BESS wanted to be awarded in all auctions between July 1,

2019, and September 29, 2019, it should have bid at least € 140,33/MW/day every

auction based on pay-as-clear design.

€ -

€ 50

€ 100

€ 150

€ 200

€ 250

€ 300

€ 350

€ 400

€ 450

€ 500

Bel

giu

m l

oca

l mar

gin

al c

apac

ity

pri

ce

[€/M

W/d

ay]

Date [dd-mm-yyyy]

72

Figure 15 shows the frequency of the continent Europe synchronous area on any day in

2018, the nominal 50 mHz frequency line, - 10 mHz line and the + 10 mHz line. The

surface between - 10 mHz and + 10 mHz indicates the dead band, which means no FCR

regulation is allowed if the frequency is in this area. Action by FCR suppliers is mandatory

if the frequency exceeds the - 10 mHz or + 10 mHz. (Elia, 2019b).

Figure 15. Continental Europe synchronous area frequency 3-3-2018 00:00 - 4-3-2018 0:00 (RTE, 2019).

The BESS discharges and charges by supplying and absorbing electricity from the grid.

The calculation of the number of cycles per year is based on the network frequency of

2018, FCR requirements and the BESS power capacity (RTE, 2019). As mentioned in

5.1.2.1.2, the minimum dimensioning of the BESS for the FCR is 10 MW/6,7 MWh

according to the FCR requirements. BES should always have sufficient energy capacity to

be fully activated for at least 15 minutes during a frequency alert state. Therefore, a

power and energy band, upwards and downwards, 2,5 MWh is reserved (Elia, 2019b).

Thus, the minimum state of charge of the battery is 2,5 MWh and the maximum state of

charge is 4,2 MWh. Given the constraints above, the BES would have made, rounded up,

362 cycles upwards and 362 cycles downwards, which is 362 full cycles per year in 2018.

362 cycles per year will be the initial situation in this research for FCR deployment with

10 MW/6,7 MWh. Details on the calculations can be found in appendix 12.3.1.

49,9

49,95

50

50,05

50,1

Fre

qu

ency

[H

z]

3-3-2018 00:00 - 4-3-2018 0:00 [min]

Frequency Minus 10 mHz Plus 10 mHz 50 mHz

73

5.4.1 Charging costs

Moments have been identified in which the BESS is exhausted but should have delivered

energy because of its obligation to the FCR. Based on the frequency data and the

constraints above, 34 MWh should have been bought in the electricity market for these

moments. The yearly charging costs from 2021-2030 are derived by multiplying the

particular forecasted day-ahead market price times the 34 MWh required per year. The

charging costs will be subtracted from the FCR income.

74

5.5 CRM contract length

In 4.1.1.8, the combinations of delivery periods and investment thresholds of different

European CRMs have been introduced and are shown again in Table 16Table 16. As

mentioned in 4.1.2.5, capacity suppliers might, depending on the investment threshold,

be eligible for CRM contracts with a maximum of 1, 3, 8 or 15 years. This study assumes

that the BESS lifetime is (at least) as long as the duration of the capacity contract. In the

end, the capacity provider is obligated to deliver the contracted capacity throughout the

entire contract period.

CRM Delivery periods [years] Investment threshold [€/MW]

United Kingdom 1 n/a

3 (T-4 auction 2021/22) 152.550

15 (T-4 auction 2021/22) 305.100

Irish all-island 1 n/a

Max. 10 300.000

Poland 1 n/a

5 (7 If C02 < 450kg/MWh) 117.647

15 (17 If C02 < 450kg/MWh) 705.882

Italy 1 n/a

15 186.000

Table 16. Investment levels of European CRMs (CREG, 2019).

The CAPEX of BES (1-hour storage depth) in this research in 2025 and 2030 are

respectively € 312/MW and € 224/MW (IRENA, 2017). Given the investment thresholds,

a BESS built-in 2025, i.e., the CAPEX of € 312/MW would obtain the longest contract in

the United Kingdom, Irish all-island and Italian CRM. Therefore, the expectation is that

BES is eligible for at least an 8-year CRM contract. This means that the BES capacity

provider could also choose for a CRM contract of less than 8 years.

75

As mentioned in 5.3.1, BES generate income by purchasing (charging) energy at low

prices and selling (discharging) at high prices. By way of illustration, Figure 16 shows

forecasted intraday market income in 2026 for a different number of cycles. In line with

expectation, the intraday market income decreases as the number of charge-discharge

cycles decreases. Therefore, it seems beneficial to make as many cycles per year as

possible.

Figure 16. Intraday market income (2026) by operating different number of cycles.

However, BES lifetime is affected by charge-discharge cycles and the lifetime is often

expressed in the (maximum) number of full equivalent cycles (Dufo-López, Lujano-Rojas,

& Bernal-Agustín, 2014). The BES lifetime (in years) may be longer by limiting the

number of cycles per year, compared to the situation where no restrictions are imposed

on the maximum number of cycles per year.

€ 22.507 € 22.032 € 21.207

€ 20.103 € 18.795

€ 17.860

€ -

€ 5.000

€ 10.000

€ 15.000

€ 20.000

€ 25.000

1039 758 606 505 433 379

Intr

aday

mar

ket

in

com

e in

20

26

[€

/MW

/yea

r]

Number of cycles [cycles/year]

76

Figure 17 shows the aggregated intraday market income over BES lifetime in years and

the corresponding maximum number of cycles per year. This research assumes 3031

equivalent cycles for Li-ion BESS in 2025 as mentioned in 5.2.1. As an example, the first

stacked bar chart of Figure 17 shows the intraday market income over three years (2026,

2027, and 2028) where the number of cycles per year is not limited by calculating the

income in Linny-R. Initially, Linny-R sets no restrictions to the number of charges and

discharges, i.e., the number of cycles, for solving the linear optimisation problem. The 3-

year lifetime follows from dividing the BES lifetime of 3031 cycles by the 1039 cycles.

Subsequently, the maximum cycles per year and the corresponding income has been

calculated for a lifetime of 4, 5, 6, 7 and 8 years.

Figure 17. Aggregated intraday market income for different BES lifetime (years).

Figure 17 shows that limiting the number of cycles per year ultimately leads to a higher

income over the entire BES lifetime. The additional income from performing more cycles

do not outweigh the income that you can earn in more years by limiting the number of

cycles per year. Therefore, it is desirable to opt for an 8-year CRM contract since this

results in a lower required capacity remuneration compared to a shorter contract.

€ -

€ 20.000

€ 40.000

€ 60.000

€ 80.000

€ 100.000

€ 120.000

€ 140.000

€ 160.000

3 years (1039) 4 years (758) 5 years (606) 6 years (505) 7 years (433) 8 years (379)

Intr

aday

mar

ket

in

com

e [€

/MW

/yea

r]

CRM contract length and number of cycles [cycles/year]

2026 2027 2028 2029 2030 2031 2032 2033

77

5.6 Scenarios

5.6.1 CRM design scenarios

Two scenarios have been drawn up due to the uncertainty regarding definitive market

CRM market design. The reference scenario can be described as a conservative scenario.

It assumes the relatively low strike proposed in Italy (€ 125/MWh), which is expected to

lead to a higher payback obligation compared to the situation with the Irish all-island

strike price. Furthermore, it is based on the last and also the smallest, least favourable,

de-rating factor of the considered European CRMs (29,4% for 1-hour storage depth). The

optimistic scenario assumes the most favourable strike price (€ 500/MWh) and de-rating

factor (39,4% for 1-hour depth of storage) that occurs in the considered European CRMs.

As mentioned in 7.3.2, the payback obligation is equal to zero with a strike price of

€ 500/MWh. This means, there is no payback obligation in the optimistic price scenario,

which leads to zero costs associated with participating in the CRM.

Parameter Reference scenario Optimistic scenario

Strike price € 125/MWh € 500/MWh

De-rating factor (1-hour storage depth) 29,4% 39,4%

Table 17. Reference scenario and experiments for calculating the required capacity remuneration.

78

5.6.2 Intraday market price scenarios

Table 18 shows the average day-ahead market price of 2018 and Elia’s predictions for

2020, 2023, 2025, 2028 and 2030 based on Figure 9 and the assumptions in 4.4.1 (Eneco,

personal information, June 26, 2019; Elia, 2019d). The last column shows the percentage

change of the day-ahead market price between the subsequent years. This percentage

change is also assumed for the intraday market price for forecasting the prices in 2020,

2023, 2025, 2028 and 2030. The hourly intraday market prices of the missing years

between 2020-2030, i.e., 2019, 2021, 2022, 2024, 2026, 2027, 2029, are approximated

through linear interpolation. For example, the price of 2019 is the average of the prices

of 2018 and 2020. In practice, this price trend could also be non-linear. However, this is

the best assumption available due to the limited data. Besides, the number of consecutive

missing years is limited and the price differences between the consecutive years is

relatively low, which is shown in appendix 12.2.1. Therefore, the impact of linear

interpolation is assumed to be limited.

Price year [year] Average day-ahead market Belgium

[€/MWh]

Percentage change regarding the

previous price [%]

2018 55,27

2020 44 -20,4%

2023 50 +13,6%

2025 49 -2%

2028 45 -8,2%

2030 51 +13,3%

Table 18. Average day-ahead market price 2018 predictions for 2020, 2023, 2025, 2028, 2030 including percentage

change.

79

5.6.2.1 Payback obligation

The market reference price is equal to the Belgium day-ahead market price as mentioned

in 4.1.2. The Belgium day-ahead market price from 2020-2030 has been calculated based

on Elia’s flexibility study average day-ahead market price forecasts as discussed in the

previous chapter (Elia, 2019d). The percentage price changes are used to calculate the

hourly day-ahead market price in 2020, 2023, 2025, 2028 and 2030 with the price of

2018 as its base year. The hourly prices of the missing years from 2020-2030 are

approximated through linear interpolation between the calculated years. The payback

obligation is calculated for 2020-2030 for the Italian strike price of € 125/MWh and Irish

all-island strike price of € 500/MWh.

As an example, Figure 18 shows the hourly Belgian day-ahead market price forecasts of

2025 with the strike price of € 125/MWh.

Figure 18. Payback obligation in 2025 with strike price € 125/MWh.

Table 19 shows the payback obligation from 2021-2030 with a strike price of

€ 125/MWh. The payback obligation from 2021-2030 is zero if the strike price is

€ 500/MWh since the maximum forecasted day-ahead market price is € 452/MWh.

Year 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Payback

obligation

[€/MW]

3380 3387 3509 3399 2246 3474 3308 3342 3387 3392

Table 19. Payback obligation with strike price € 125/MWh (2021-2030).

€(50)

€-

€50

€100

€150

€200

€250

€300

€350

€400

€450

Bel

gian

ho

url

y d

ay-a

hea

d m

ark

et p

rice

[€

/MW

h]

1-1-2025 00:00 - 31-12-2025 23:00 [hours]

80

5.6.3 FCR price scenarios

This research assumes that the FCR price is not expected to rise in the future and are

more likely to fall as discussed in 4.4.2. The following three price scenarios are taken into

consideration:

1. The FCR price is the average price since the introduction of the regional FCR

auction on July 1, 2019, till September 30, 2019: € 215,83/MW/day;

2. The FCR price is the lowest price since the introduction of the regional FCR auction

on July 1, 2019, till September 30, 2019: € 140,33/MW/day;

3. The FCR price is 50% of the (average) FCR price in the first price scenario:

€ 106,78/MW/day;

The first two price scenarios reflect the prices observed today and the third scenario

shows the price in an extreme case.

81

5.7 Experiment design

This section discusses the experiment design of modelling the required capacity

remuneration for BESS. BESS is assumed to operate in the intraday market, FCR market

or in both the intraday and FCR market. The experiments takes CRM design scenarios,

intraday market scenarios and FCR scenarios into consideration as discussed in 5.6 and

the corresponding expected storage costs from 5.4.1. The required capacity

remuneration will be modelled for the photo years 2020, 2025 and 2030. The years are

briefly described in the table below. The BESS will be built in 2020, 2025 and 2030 and

are in operation from respectively 2021, 2026 and 2030 till the end of the BESS its

lifetime. The BESSS lifetime is equal to the CRM contract of 8 years as mentioned in 5.5.

Year Description

2020 Competitiveness of BES in today’s energy market with (theoretically) a CRM

2025 Competitiveness of BES in the first CRM auction

2030 Competitiveness of BES in the 6th (T-4/T-1) CRM auction

Table 20. Year description of scenarios 2020, 2025 and 2030.

Table 21 shows the experiments for a BESS (10 MW/10 MWh) operating in the intraday

market. The price scenario Elia DA is the price scenario as discussed in 5.6.2 which is

based on Belgian wholesale market forecasts from Elia. Reference and optimistic refer to

the CRM design scenarios introduced in 5.6.1.

Year CRM design scenario Price scenario

2020 Reference Elia DA

Optimistic Elia DA


Optimistic Elia DA


Optimistic Elia DA

Table 21. Experiments for BESS (10 MW/10 MWh) operating in the intraday market.

82

Table 22 shows the experiments for a BESS (12,5 MW/10 MWh) operating in the FCR

market. Average, minimum and 50% refer to the FCR price scenarios as described in 5.6.3.

Year CRM design scenario Price scenario 2020 Reference Average

Optimistic Average Reference Minimum Optimistic Minimum Reference 50% Optimistic 50%

2025 Reference Average Optimistic Average

Reference Minimum

Optimistic Minimum

Reference 50%

Optimistic 50%

2030 Reference Average

Optimistic Average

Reference Minimum

Optimistic Minimum

Reference 50%

Optimistic 50%

Table 22. Experiments for BESS (12,5 MW/10 MWh) operating in the FCR market.

Table 23 shows the experiments for a BESS (12,5 MW/10 MWh) operating in both the

intraday and FCR market. As explained in 5.2.1.2, 6,7 MWh of the total 10 MWh BESS

energy capacity is reserved for the FCR operation and 3,3 MWh is used for energy time-

shifting in the intraday market.

Year CRM design scenario Price scenario

2025 Reference Minimum

Optimistic Minimum

Table 23. Experiments for BESS (12,5 MW/10 MWh) operating in both the intraday and FCR market.

83

6 Verification and validation

6.1 Verification

The Linny-R model for calculating the intraday market income will be verified to evaluate

if the model performs as it is intended to. Model verification concerns with building the

model right (Balci, 1994). The Linny-R model should find the optimal moments for buying

and selling electricity to maximise profit. Figure 19 shows the behaviour of the Linny-R

model for the year 2026 during the first 28 time steps, i.e., hours.

Figure 19. Verification of Linny-R model of 2026.

The number one indicates whether an operation is active in its specific hour. The second

column represents the activity of discharging and third column represents charging. The

fourth column shows the day-ahead market price during its corresponding hour. As

discussed in 5.3.1.1, the considered optimisation and look-ahead period in this research

of receptively 1 hour and 8 hours are taken into account. The green blocks represent the

optimisation period and the yellow blocks the look-ahead period. The numbers in the

green blocks indicate the number of optimisation stages. The first operation is done by

the BESS in hour 6.

84

The model then decides whether to charge and thus buy electricity for € -29,96/MWh,

i.e., receiving instead of paying money because of the negative electricity price and looks

ahead eight prices. € -29,96/MWh is the best price given the stage length.

Now that the BESS has been charged it seeks the best moment to sell the electricity while

considering the roundtrip efficiency losses, which is found at hour 20. The price at hour

20 is the highest of the prices from hour 20 – hour 28. Thus, the Linny-R model simulates

the behaviour that can be expected from energy time shifting. However, it should be kept

in mind that the BESS could earn more income by charging and discharging more

frequently. The BESS is constrained to 378 cycles per year due to the BESS cycle life and

the 8-year CRM contract as explained in 5.5, which means the battery charges and

recharges less often.

85

6.2 Input validation

The input validation considers whether storage costs estimated in this resarch

correspond somewhat to the actuals storage costs of realized projects. The CAPEX of the

BESS is based on IRENA’s electricity storage costs outlook from 2017 (IRENA, 2017).

According to Navigant Research, the price of 10/40MWh BESS is around $15,8 million in

2019 which almost corresponds to IRENA’s estimates (Eller, 2019). However, it is

difficult to find out what the actual costs are as developers in press releases often present

approximated project costs. The published (approximated) costs of projects in Lake

Bonney (Australia), Cremzow (Germany) and Carboneras (Spain) plus the estimated

storage costs in 2016 by IRENA (2017) are shown in Table 24

Location

(start

construction)

Power

capacity [MW]

Energy capacity

[MWh]

Total project

costs [M€]

IRENA’s estimated

storage costs in 2016

[M€]

Lake Bonney (2018) 25 52 23,4 25,6

Cremzow (2018) 22 34,8 17 17,9

Carboneras (2017) 20 11,7 11,5 7,8

Table 24. Published (approximated) project costs by developers compared to IRENA’s estimated storage costs (Infigen, 2018; Enel, 2019; Endesa, 2017; IRENA, 2017).

Assuming that the project quotations for projects starting construction in 2018 are made

in 2017, the prices published by developers and IRENA seem to be quite similar in the

first two examples. The prices of IRENA are somewhat higher since these were the prices

for 2016. There is a greater difference between the published project costs and IRENA in

the last example. This could be due to a too high estimate by the developer or a more

expensive supplier of the battery packs. On the other hand, it is most likely due to a

smaller power/energy capacity dimension of the BESS in the first two examples. The

calculation method of IRENA determines the CAPEX based on power and energy capacity.

However, the dimensioning of the power and energy capacity and the CAPEX is not an

exact linear relationship. Economies of scale effects occur since greater dimensioning of

the power and energy capacity leads to cost advantages. Therefore, the IRENA costs

outlook might more accurate for greater BES dimensions and the costs of the considered

10 MW BESS is probably higher. IRENA’s outlook is based on data of storage projects. If

these projects were mainly larger projects, this makes sense. An accurate estimate of the

storage costs can ultimately be obtained by requesting quotes from manufacturers

86

6.3 Model validation

Validation concerns with building the right model (Balci, 1994). Thus, whether the

assumptions that have been made represent the real-world system with sufficient. The

calculated intraday market income have been compared with realized results by Eneco’s

48 MW BES system in Germany. The results seem to be somewhat similar, taking into

account the power and energy capacity that has been deployed by Eneco in the intraday

market in the best possible way (Eneco, personal information, October 17, 2019).

However, the realized income is lower compared to the modelling results. The difference

can largely be explained by the fact that in the Linny-R model, the maximum amount of

energy that can be purchased or sold is equal to the BESS energy capacity, i.e., 10 MWh.

In reality, this does not happen. Smaller volumes are bought and sold, which is less

economically optimal compared to the optimization by Linny-R and leads to a lower

income.

87

7. Results

In this chapter, the competitiveness of BESS in the intraday market and in the FCR market

will be discussed according to the NPV and the required capacity remuneration for the

years 2020, 2025 and 2030. The calculation is based on a CRM contract 𝑁 of 8 years and

a real rate of return 𝑟𝑟 of 5,7% (based on the nominal rate return 𝑟𝑛 7% and inflation rate

𝑟𝑖 1,26%). The competitiveness has been assessed in the reference scenario and the

optimistic scenario as presented in the experiment design.

88

7.2 Competitiveness in the intraday market

The NPV for both the reference scenario and the optimistic scenario has been calculated

for the years 2020, 2025 and 2030 and are shown in Figure 20. The NPV is negative in all

the situations, which means the projects will result in a net loss and the investment

should be rejected. Thus, BES is not competitive in today’s market nor in the future

without any capacity remuneration. The decrease in NPV for the different years is mainly

because of the strong storage costs reduction and to a lesser extent to the income

increase. The CAPEX has been decreased by 48% between 2020-2030 while the intraday

market income has increased only by 17%.

Figure 20 shows the required capacity remuneration in the reference scenario and the

optimistic scenario for the years 2020, 2025 and 2030. The lower required capacity

remuneration in the optimistic scenario is mainly because of the higher de-rating factor

and because of the zero payback obligation. As mentioned in 2.3.1, the auction clearing

price in Irish all-island and United Kingdom’s capacity auctions with the presence of BES

varied from € 6,80 – 46,15/kW/year in 2018 and 2019. This implies the BESS are built

before the delivery years 2018 and 2019. Even with the significant storage costs

reductions expected in 2020, 2025 and 2030, BES is most likely not competitive in the

intraday market.

Figure 20. Required capacity remuneration, given the day-ahead market income in the reference scenario and in the optimistic scenario (2020, 2025, 2030).

€ 243

€ 179

€ 154

€ 112

€ 93

€ 66

€ -

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Reference scenario Optimistic scenario

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/yea

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2020 2025 2030

89

7.2.1 Doubling the depth of storage

As mentioned in 4.1.2, energy-constrained capacity providers are awarded in the CRM

according to their corresponding de-rating factor, which depends on the depth of storage.

The greater the contribution towards adequacy, the greater the de-rating factor and thus

the greater the capacity remuneration. Furthermore, a greater depth of storage could

generate more income by capturing price spreads more effectively given the same

amount of (charging and discharging) cycles. However, greater depth of storage entails

additional CAPEX costs, more specifically energy capacity costs, and O&M costs. The

question is whether the additional intraday market income capacity outweighs the

additional storage costs.

The average intraday market income from 2026-2033 (2025 scenario) increases by 27%

if the storage depth doubles. In this research, the 10 MW/10 MWh BESS will become a 10

MW/20 MWh BESS, which means the CAPEX energy installation costs increase because

of the additional 10 MWh. The total CAPEX, consisting of the energy and power

installation costs increases 75% with the 10 MW/20 MWh BESS compared to the 10

MW/10 MWh. The additional intraday market income does not outweigh the extra

storage cost, thus doubling the storage depth leads to a larger negative NPV of the project.

The required capacity remuneration increases from € 154 to € 163/kW/year. Therefore,

more storage depth in 2025 will not increase the competitiveness of BES.

90

7.2.2 Conclusions

Figure 20 has shown the relatively high required capacity remuneration for the years

2020, 2025 and 2030 in the reference and the optimistic scenario. Given capacity

remunerations below € 50/kW/year in other European CRMs, which includes prices near

zero in the United Kingdom, it is unlikely that BESS is competitive if it operates in the

Belgian intraday market.

Figure 21 shows the breakdown of the EBIDTA for 2020, 2025 and 2030 in the reference

scenario. It includes the accumulated intraday market income and variable costs, i.e.,

O&M costs and the payback obligation during the 8-year CRM contract. The figure clearly

shows the relatively high variable costs compared to the income, which has a major

influence on the competitiveness of BESS.

Figure 21. Breakdown of the EBIDTA for 2020, 2025 and 2030.

-

200

400

600

800

1.000

1.200

1.400

1.600

2020 2025 2030

Acc

um

ula

ted

eu

ros

ov

er 8

yea

rs [

1*1

03

€]

Intraday market income O&M costs Payback obligation

91

7.3 Competitiveness in the FCR market

The NPV and the required capacity remuneration will be discussed in this chapter per

FCR price scenario. The price scenarios, as presented in 5.6.3, will again be briefly

explained.

7.3.1 Average FCR price scenario

The price in the average FCR price scenario is the average capacity price between the

introduction of the regional FCR auction on July 1, 2019 and September 30, 2019. The

yearly remuneration is equal to the average FCR price of € 215,83/MW/day multiplies

by 10 MW nominated power capacity in the FCR auctions, as assumed in the scenarios,

times 365 days per year. Thus, the capacity provider will receive € 787.792/year in the

average FCR price scenario during the 8-year CRM contract. The other two scenarios are

also calculated in the same way.

Given the average FCR price, the NPV is negative in 2020, which means the investment

result in a nett loss and should be rejected. The NPV in the optimistic scenario and both

2025 and 2030 scenarios are positive which implies no capacity remuneration is

necessary. It can be derived from Figure 22 that BESS seems to be competitive in the FCR

market in 2025 and 2030 given the average FCR price scenario. However, it is not very

likely that the current average FCR price is equal to the average FCR price in 2025 and

2030 given increasing competitiveness in the FCR market due to replacement of baseload

nuclear capacity by flexible generation capacity, as discussed in 5.4.3.

Figure 22. Required capacity remuneration, given the average FCR price in the reference and optimistic scenario (2020, 2025, 2030).

€ 50

€ 35

€ - € -€ - € -€ -

€ 10

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€ 60


Req

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[€

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Average FCR price (2020) Average FCR price (2025) Average FCR price price (2030)

92

7.3.2 Minimum FCR price scenario

The price in the minimum FCR price scenario is the minimum capacity price between the

introduction of the regional FCR auction on July 1, 2019 and September 30, 2019, which

is € 140,33/MW/day. The FCR price, and thus the income, is 35% lower compared to the

previous average FCR price scenario. The NPV of the project is negative for both scenarios

in 2020, 2025 and 2030. However, the year 2025 is still more important than 2020 since

the remuneration starts from November 2025.

Figure 23 shows the required capacity remuneration is limited in 2025 to € 55/kW/year

in the reference scenario and € 39/kW/year in the optimistic scenario. In the previous

price scenarios, the de-rating factor and a high strike price did not seem to have a decisive

role. However, in this price scenario, the 11 euros that you need less in the 2025

optimistic scenario compared to the reference scenario may be decisive whether the

business case of BES is competitive, given the capacity remunerations in recent European

CRMs (€ 6,80 – 46,15/kW/year). BES might be competitive from 2025 if the FCR capacity

price is equal to the lowest price observed in today’s FCR market. However, the results

are not convincing and too dependent on favourable parameters and capacity prices.

Figure 23. Required capacity remuneration, given the minimum FCR price in the reference and optimistic scenario (2020, 2025, 2030).

€ 144

€ 105

€ 55

€ 39

€ - € -€ -

€ 20

€ 40

€ 60

€ 80

€ 100

€ 120

€ 140

€ 160


Req

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[€

/kW

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Minimum FCR price (2020) Minimum FCR price (2025) Minimum FCR price (2030)

93

7.3.3 50% Average FCR price scenario

The price in the 50% average FCR price scenario is 50% of the average capacity price

between the introduction of the regional FCR auction on July 1, 2019 and September 30,

2019, which is € 106,78/MW/day. This means the FCR income decreased again by 24%

compared to the previous scenario. Now the NPV of the project is negative in all situations

and dependent on capacity remuneration as in Figure 24. BES is most likely not be

competitive at all in 2025 because of the high required capacity remuneration. The

required capacity remuneration in 2030 looks favourable, but it is uncertain how the FCR

prices will develop over time. The FCR market may already be saturated by that time

and the capacity remuneration is already heading towards zero, as seen in the United

Kingdom CRM auction.

Figure 24. Required capacity remuneration given 50% of the average FCR price in the reference and optimistic scenario (2020, 2025, 2030).

€ 184

€ 135

€ 95

€ 69

€ 32 € 21

€ -

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Req

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50% Average FCR price (2020) 50% Average FCR price (2025) 50% Average FCR price (2030)

94

7.3.4 Revenue stacking of FCR and intraday market income

In this research, the BESS dimensioning operating in the FCR market is

12,5 MW/10 MWh as mentioned in 5.2.1.2. However, only 6,7 MWh of the 10 MWh is

continuously reserved for FCR provision in this research. The unused energy capacity of

3,3 MWh is reserve capacity for charging and discharging moments and can be used most

of the time for energy time-shifting in the intraday market. No extra power capacity is

required for energy time-shifting while being active in the FCR auction. The additional

25% power capacity of 2,5 MW is only used during moments when charging or

discharging is required in the FCR market. Thus, the BESS dimensioning will be same

when operating in the FCR and intraday market. Figure 25 shows the required capacity

remuneration if the BESS operates only in the FCR market versus operating in both the

FCR market and the intraday market. The required capacity remuneration decrease by

36% because of the additional income from the intraday market. Therefore, revenue

stacking has a significant impact on competitiveness.

Figure 25. Combining intraday market and FCR incomes.

€ 55

35

€ -

€ 10

€ 20

€ 30

€ 40

€ 50

€ 60

Minimum FCR price (2025) Minimum FCR price + intraday market income(2025)

Req

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Reference scenario

95

7.4 Conclusions

The future looks bright for BESS from 2025 and onwards, given today’s average FCR

price. However, if the average FCR price decrease 35% to the minimum FCR price

measured today, BES is expected to be a lot less competitive. Halving the average price

today would be disastrous. The stronger competitive position in the FCR market comes

mainly from the higher FCR income compared to the intraday market income. The

maximum yearly intraday market income is compared in Table 25. The FCR income is

almost two times higher compared to the intraday income in every price scenario. This is

also the main reason for the difference in competitiveness between operating in the two

markets. Revenue stacking by combining the FCR and intraday market income would

increase the competitiveness of BES and can be concluded to be necessary.

Average intraday income

[€/MW/year]

Average / minimum / 50% average FCR scenario

[€/MW/year]

2020 16.123 78.779 / 51.221 / 39.390

2025 18.780 78.779 / 51.221 / 39.390

2030 20.376 78.779 / 51.221 / 39.390

Table 25. Income differences in the intraday market and the FCR market (2020, 2025, 2030).

96

8 Discussion

8.1 Results

8.1.1 Intraday market income

The intraday market income has been calculated based on the VWAP intraday market

prices. The initial data intraday market data set of 2018 consists of 275.627 deals and the

hourly VWAP price consists of 1-77 deals. The VWAP of a specific hour could include a

great variety of deal prices, which is illustrated in Table 26.

Date Volume [MWh] Price [€/MWh]

01-01-2018 01:00 0,2 20

01-01-2018 01:00 0,2 30

01-01-2018 02:00 19,4 5

01-01-2018 02:00 0,1 2

01-01-2018 02:00 10 2

01-01-2018 02:00 15 -10

Table 26. Deals on the EPEX intraday market 1-1-2018 02:00.

Ideally, discharging, i.e., selling, would be favourable in the intraday market for the

€ 30/MWh and charging, i.e., buying, for the price of € -10 /MWh. However, there is no

possibility in practice to capture these price spreads observed here. In practice, it is likely

that there would already have been a purchase in the second hour for € 2/MWh because

of the relatively low price. This means that there will no longer be bought for € -10/MWh.

This also applies vice versa: the transaction of the energy capacity might have already

occured in the first hour for € 20/MWh instead of € 30/MWh. The VWAP on January 1,

2019, 01:00 is € 25/MWh, and 2:00 is € -0,74/MWh. Thus, Calculating the intraday

market income based on the VWAP prices means that there is a loss in price volatility

compared to reality where there is a possibility capturing this price volatility.

97

Besides, electricity market price volatility is expected to increase between 2030 – 2040

in Belgium because of the larger penetration of renewables, e.g., solar and wind energy,

and relying more on demand-side response (DSR) to maintain adequacy (Elia, 2017b).

However, the magnitude of the increase in price volatility is unclear. Therefore, the

expected price volatility after 2030 has not been taken into account. Both reasons could

underestimate the intraday market income. On the other hand, the optimization model in

Linny-R calculates the optimal buy and sell moments assuming you know all the VWAPs

which leads to perfect foresight, which will be further discussed in 8.2.2.2. To conclude,

the intraday market income may be underestimated when using VWAP. However,

considering the VWAPs compared to taken all the inviduals deals into account leads to a

more releastic approach.

8.1.2 Payback obligation

The payback obligation in this research depends strongly on the strike price of the

reliability option in the CRM. A strike of € 125/MWh results in payback obligation from

2021-2038 while if the strike price is € 500/MWh, there is no payback obligation at all.

This is due to the maximum forecasted day-ahead market price of € 452/MWh as

mentioned in 5.6.2.1. Table 27 shows the influence of the payback obligation on the

required capacity remuneration, given in the intraday market income and minimum FCR

income in 2025. Assuming that the strike price would not drop below the € 125/MWh,

the greatest influence of the payback obligation on the required capacity remuneration

would be € 3/kW/year. A difference of € 3/kW/year may not have a decisive role in the

investment decision in BES.

In the reference scenario Required capacity remuneration [€/kW/year]

With payback obligation Without payback obligation

Intraday market income (2025) 154 151

Minimum FCR income (2025) 55 52

Table 27. Influence of the payback obligation on the required capacity remuneration.

98

8.1.3 FCR income

As mentioned in 4.4.2, there are no clear forecasts on the FCR price development.

However, the price is not expected to rise and more likely to fall in the future. The FCR

prices are strongly demand-driven, and it is hard to model as stable income streams.

Furthermore, the FCR income rests on the assumption that the BESS is awarded in every

FCR auction. In practice, there might be moments that the FCR provider is not awarded

in the auction because of relatively low bids of other participants or unforeseen

circumstances.

99

8.2 Limitations

8.2.1 Data

All data used for the development of price scenarios in this study is publicly accessible

and thus auditable. However, there are also research organisations, such as Bloomberg

New Energy Finance (BNEF), which have also investigated electricity price developments

and storage costs. These paid reports may not be published in this master thesis, so

therefore there might be relevant data missing. Also, Eneco has electricity price forecasts

that have not been included in the report due to competition sensitivity. Therefore, in a

master thesis there is a limitation as to what is available to the public. By being allowed

to use data from, for example, BNEF and Eneco, the reliability of the research can be

increased. This could also be done by requesting quotes from storage costs. However, as

mentioned in the validation, the storage costs estimates do not differ very great from the

storage costs of realized projects.

8.2.2 Method

8.2.2.1 Net Present Value

The financial model for calculating the NPV of the project and after that, the required

capacity remuneration, are performed in Excel. NPV is the most common technique for

evaluating the profitability of a project. However, there are also drawbacks to this

method. The calculation of the NPV is based on the required rate of return, which is

chosen by the investors and often based on the risk of the investment, company’s

experience with similar projects, set by managers or a rule of thumb (Liljeblom, &

Vaihekoski, 2004). The NPV can be unreliable if it is incorrectly selected due to inaccurate

(risk) estimates. This research assumes a real rate of return 𝑟𝑟 of 5,7% based on the

nominal rate return 𝑟𝑛 7% and inflation rate 𝑟𝑖 1,26%. However, the nominal rate of

return is a rough estimate. A risk profile can be drawn up based on this specific

investment taking into account the likelihood of the related income and costs. This can

lead to a better estimate of the required rate of return. The uncertainty about the future

FCR and intraday market prices may lead to a higher nominal rate of return. A different

real rate of return influences the NPV, the required capacity remuneration and thus the

perception of the competitiveness of BES.

100

8.2.2.2 Linny-R

As mentioned in the model description, the intraday market income has been calculated

with the use of a Linny-R model, which determines the optimal buy and sell moments

over a certain period. The model assumes that there is knowledge into the intraday

market prices a few hours in advance. This look-ahead period, which is assumed 8 hours

in this research, reflects the time that, on average, traders look ahead when trading.

However, the model looks ahead for 8 hours of realized intraday market prices, which

causes perfect foresight.

The look-ahead period is halved to evaluate the influence of the look-ahead period on the

intraday market income. This leads to the fact that the Linny-R model looks ahead 4

intraday market prices instead of 8 intraday market prices. The average intraday income

decreases by 4% from 2026-2034 since the model buys and sells less optimally compared

to the previous situation. The average intraday income from 2026-2034 has decreased

by 4%. The required capacity remuneration in 2025 increases from € 154 to

156/kW/year with a look-ahead period of 4 hours instead of 8 hours. Therefore, the

difference in the look-ahead period does not have a major impact on the competitiveness

of BES.

8.2.3 Model and analysis

The NPV method can be seen as a common method for assessing the profitability of BES

in electricity markets based on the number of studies found in literature (Oudalov,

Chartouni, & Ohler, 2007; Cho, & Kleit, 2015; Mercier, Cherkaoui, & Oudalov, 2009).

Striking is that none of these studies takes corporate tax rate into consideration when

calculating the NPV. The Belgian corporate tax rate is 25% in 2020 as mentioned in 5.2.1

and has a significant impact on the NPV. By way of illustration, the required capacity

remuneration in 2025 (reference scenario) decrease by 8% for a BESS operating in the

intraday market if the corperate tax is not taken into consideration. Therefore, a

disadvantage of the NPV method is that only projects can be compared with each other

where the same assumptions have been made.

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8.3 De-rating factor methodology

This chapter reflects on methodologies for determining de-rating factors of BES in the

Belgian CRM. First, Elia’s methodology for determining the de-rating factors of the

Belgian CRM will be described. Subsequently, three operation strategies of BES will be

discussed. Lastly, the methodology in the United Kingdom will be discussed.

8.3.1 Belgian methodology

An overview of the process for determining the de-rating factors of the Belgian CRM is

shown in Figure 26. The different steps are explained below.

Figure 26. Process overview of the methodology for determining the de-rating factors of the Belgian CRM (based on Elia, 2019e).

The first step involves selecting the input scenario that is used to calculate the de-rating

factors. Consumption growth and hourly consumption profiles, weather profiles, energy

limitations and technology specifications, renewable energy capacity, thermal capacity,

storage capacity, market response capacity and cross-border capacity between market

zones are taken into consideration in the input scenario (Elia, 2019e).

Based on the input scenario, the statistical method Monte Carlo simulation is used to

solve the unit commitment problem, which refers to scheduling the generation units to

meet the generation demand while minimizing the operation costs (Valenzuela, &

Mazumdar, 2001). The main variables of interest are the hourly generated energy per

technology and the hourly exchange (i.e., import and exports) between market zones and

these will be derived from the model output. Virtual generation capacity of 100% uptime

is added to the system if the input scenario does not meet the Belgian adequacy criteria

of 3 hours Loss of Load Expectation (LoLE)5 in the simulation (or removed in case of

surplus capacity) (Elia, 2019e).

5 “Represents the number of hours per annum in which, over the long-term, it is statistically expected that supply will not meet demand’’ (DECC, 2013, p. 3).

Input scenarioModel

simulation

Near-scarcity hours

identification

Calculating of de-rating

factors

Cross-border contribution

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Subsequently, the relevant hours for the adequacy in Belgium, i.e., near-scarcity hours,

are determined based on the model output. Near-scarcity hours refers to both hours with

energy not served (i.e., scarcity hours) and hours in which any additional domestic load

will lead to Expected Energy Not Served (EENS). Domestic capacity and import capacity

from Belgian’s directly connected market zones, i.e., the Netherlands, United Kingdom,

Germany, and France, are taken into account (Elia, 2019e).

The contribution of thermal generation units, e.g., CHP, OCGT and CCGT plants, to the

Belgian adequacy is independent of weather conditions, activation limits and energy

limitations. Only annual forced outage rates and the corresponding duration derived

from historical input (scenario) data are taken into account for calculating the de-rating

factors of these technologies. The de-rating factor of thermal technologies (%) is equal to

100% minus the specific assumed forced-outrage rate (%) of the thermal technology

(Elia, 2019e).

In comparison with the thermal technologies, the de-rating factors of energy-constrained

technologies cannot be derived from historical data. The energy-constrained de-rating

factors are calculated based on their contribution during near-scarcity hours, where the

contribution is derived from the model output of a Monte Carlo simulation including all

technologies. The model optimizes the costs of energy-constrained technologies so that

the income from discharge is higher than the costs of discharge, which leads to a

maximum contribution during near-scarcity hours. The de-rating factor of energy-

constrained technologies (%) is its average contribution during near-scarcity hours

(MW) divided by its reference power (MW) (Elia, 2019e).

As mentioned earlier in 4.1.1.2, SLA categories are defined, which consists of energy-

constrained technologies with the same hourly activation constraints and assumed to be

activated once a day. Each SLA category is associated with a specific de-rating factor and

represents the contribution of the category to the adequacy. Elia will publish the de-

rating factors in their yearly auction report (Elia, 2019e).

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In the last step, the cross-border contribution has been calculated based on the model

output. The average contribution of the directly connected market zones to adequacy is

determined by maximum capacity contribution, i.e., capability, of the directly connected

market zones to export during near-scarcity hours (Elia, 2019e). Belgium relies strongly

on cross-border capacity to ensure its necessary level of adequacy. Furthermore, scarcity

moments in Belgium are strongly correlated to scarcity moments in one of the directly

connected market zones and is likely to increase in the future (Elia, 2019d).

8.3.2 BES operation strategies

European countries with a capacity mechanism are obligated to have reliability standards

which indicate their security of supply requirement. The country’s adequacy indicators

include at least the LoLE and the EENS (European Parliament and of the Council, 2019).

Both indicators are also taken into consideration in the Belgian CRM law and Elia’s latest

adequacy study (Belgische Kamer van volksvertegenwoordigers, 2019a; Elia, 2019d).

EENS provides a better indication of the fiscal damage of shortage periods, while the LoLE

does not reflect on the severity of the shortage period. LoLE indicates only the period, i.e.,

hours, with the presence of EENS. One is indifferent which adequacy parameter is used if

the electricity in the market is only supplied by solely dispatchable generators. This is

because there is a direct relationship between the EENS and LoLE values since the

varying level of capacity in the electricity market. However, there is no direct relationship

anymore between EENS and LoLE for energy-constrained technologies, e.g., BES. BES has

a (maximum) energy stored, so the reduction in EENS is more or less the same. BES can

respond in different ways to EENS periods where each difference can lead to a different

impact on the reduction in LoLE (Dent, Wilson, & Zachary, 2017).

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Figure 27 presents three alternatives of the contribution of BES to reduce shortages. The

dotted line with a parabolic shape represents the severity of the shortage, and the

shortage occurs if the dotted line exceeds the x-axis. The area between the x-axis, the red

line and the dotted line represents the energy provided by BES. The energy provided by

BES and thus the reduced EENS. The red arrows indicate the Loss of Load (LoL) duration

(Dent, Wilson, & Zachary, 2017).

Figure 27. Alternative contributions of BES to shortage periods (Based on Dent, Wilson Zachary, 2017).

In alternative A, the LoL is not reduced during the shortage period. However, BES

brings down the maximum energy not served peak. The indicator LoLE actually

underestimates the contribution of BES in this case (Dent, Wilson, & Zachary,

2017).

In alternative B, the LoL duration is minimized by deploying BES during moments

when the EENS is minimal. The BESS only contribute to periods where it can

totally reduce the energy not served. However, reliable, inexhaustible capacity

(i.e., firm capacity) which would achieve the same LoL reduction as BES in this

example would supply energy during the entire shortage period. Thus, firm

capacity would limit the energy not served more than BES. Now, the LoLE

overestimates the contribution of BES (Dent, Wilson, & Zachary, 2017).

In alternative C, BES delivers energy at a steady rate during the shortage period.

The same reduction of LoL and EENS would be achieved by firm capacity. The

LoLE values BES and firm capacity in the same way (Dent, Wilson, & Zachary,

2017).

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As shown in Figure 27, the three alternatives for the deployment of BES lead to different

reduction of the LoL duration given an equal availability of storage. Therefore, the most

appropriate adequacy indicator for the contribution of BES is EENS because it is not

sensitive to the strategy of BES deployment during shortage periods and represents the

economic damages directly (Dent, Wilson, & Zachary, 2017; National Grid, 2017). The

deployment of BES on the electricity market, e.g., timing and energy delivery profile, is a

commercial decision. Therefore it is difficult to determine which strategy the storage

operator will choose during shortage periods (National Grid, 2017).

Note that the presence of storage is still limited in the Belgian electricity market.

Therefore, the available EES does not significantly affect the direct relationship between

LoLE and EENS, as discussed earlier. Thus, the LoLE is still appropriate for determining

the required capacity, i.e., level of security to be achieved by the Belgian CRM (Dent,

Wilson, & Zachary, 2017; Elia, 2019e).

8.3.3 UK methodology

The required capacity, i.e., level of security to be achieved by the Belgian CRM, is

calculated according to the LoLE adequacy criteria (Belgische Kamer van

volksvertegenwoordigers, 2019a). Besides, the input scenarios in the Belgian de-rating

methodology also relies on the same LoLE as mentioned in 8.3.2.1.

However, it is more appropriate to calculate the contribution of BES according to the

EENS adequacy criteria, as explained in the previous subsection. The de-rating

methodology for energy-constrained capacity providers used in the United Kingdom is

actually based on the EENS metric (National Grid, 2017). Research by National Grid

(2017) has shown that using the LoLE or EENS metric for calculating the contribution of

storage is significantly different. Given BES is deployed as described in alternative C and

taken into account that the input scenario is the same, the de-rating factor would be

29,9% (1-hour storage depth) if the methodology is based on the EEU, while the de-rating

factor is 47,0% if the methodology is based on the LoLE (National Grid, 2017).

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Besides, National Grid (2017) showed that the de-rating factor for a certain storage depth

will decrease as the corresponding penetration of energy-constrained storage increases

since the contribution to the security of supply decreases. Energy-constrained storage

will cause an increase in the LoL duration, which means the contribution of that specific

energy-constrained storage will saturate, and thus the de-rating factor will decrease

(National Grid, 2017).

8.3.4 Conclusions

It can be concluded from previous subsections that calculating the contribution of BES

based on the adequacy indicator EENS is most appropriate. In the Belgian methodology,

the contribution is of storage is based on the LoLE, which is beneficial for storage

operators in terms of the de-rating factor as illustrated in the results above. The

methodology may be adjusted as storage will play a dominant role in the Belgian energy

supply. After all, this has also happened in the UK. Second, the de-rating factors are

expected to decrease as the penetration of storage increases. In the light of both findings,

it is therefore favourable for BES to participate in (one of the) first capacity auctions

where the de-rating value may still be favourable for energy-constrained technologies.

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9 Conclusions

This section includes the conclusion of the research on the competitiveness of BES in the

future Belgian market. First, the sub-questions of this research, as introduced in 3.1, will

be answered. Finally, the main research question will be answered. Hereafter,

recommendations for further research will be presented.

9.1 Answers to the sub-questions

What are the characteristics of the Belgian Capacity Remuneration Mechanism?

The capacity remuneration mechanism proposed in Belgium is the (market-wide) central

buyer capacity mechanism based on reliability options. The Belgian CRM market rules

and auction rules are not known yet, and thus a selection has been made of the most likely

characteristics, based on interim publications by parties involved and other CRMs in

European countries. The market reference price, strike price and de-rating factors have

been identified as the most important parameters for required capacity remuneration.

The further elaboration of the mechanism on these parameters is therefore important for

the competitiveness of BES.

Which EES technologies fit the characteristics of the Belgian capacity market best?

Various studies support that Li-ion battery storage is the best alternative for operating in

the Belgian CRM. The technology is characterized by relatively low CAPEX, it still

undergoes a major cost reduction, has a fast response time and meets the ancillary

services requirements, and projects are relatively easy to realise. Therefore, Li-ion

storage is abundant on ancillary services markets and common in other European CRMs.

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What is the revenue derived from the wholesale electricity market and the


The intraday market is preferred over the day-ahead market for BES due to the greater

price volatility. The price volatility is likely to increase in the future due to the large share

of renewables and relying more on DSR to maintain the adequacy in Belgium. This

development offers opportunities for BES in the future since it may lead to potentially

higher income. However, no studies have been found on the price development of the

Belgian intraday market.

Capacity providers in the Belgian CRM auction are obligated to pay the difference

between the market reference price, i.e., Belgian day-ahead market price, and a certain

strike price whenever the day-head market price exceeds the strike price. No Belgian

strike price has been mentioned during CRM meetings. The lowest strike price proposed

in Europe is € 125/MWh. This strike price would only leads to € 3/kW/year difference

in required capacity remuneration compared to a CRM without strike price. Therefore,

the payback obligation is not expected to have a significant impact on the investment

decision in BES.

What is the revenue derived from ancillary services?

BES would not require any capacity remuneration from the first capacity delivery year if

the FCR price is equal to today’s average FCR price. However, the research assumes that

the FCR price is not expected to rise in the future and more likely to fall because of the

increase of flexible generation capacity and the further integration of European ancillary

service markets. The FCR price is likely to decline when the new ancillary service

products will be implemented in July 2020. It is advisable to follow the FCR price

development after this introduction. There is a lot of uncertainty about the future FCR

prices and therefore the prices of the FCR scenarios vary widely (215,83 – 140,33 –

106,78 €/MW/day). Still, the FCR price will have to fall significantly to reach a

comparable level of the intraday market price. Even if 3,3 MWh of the BESS energy

capacity is actually unused, as explained in 5.2.1, the FCR market is still preferred over

the intraday market.

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What is the remuneration required for EES in Belgian capacity remuneration

mechanism auction?

It is likely that the Belgian auction clearing price could drop to near-zero based on the

auction results of other European CRMs. In this case, BES must be able to compete with

probably (existing) gas plants. BES solely operating in the intraday market would not be

competitive in the Belgian CRM auctions given the required capacity remuneration of at

least € 66/kW/year in 2030. BES does not need capacity remuneration from 2025

onwards with today’s average FCR price. This is due to the expected fall in battery prices

up to 2025. However, it is not very likely that the capacity prices stays the same. The

competitiveness of BES can, however, be improved by trading in the intraday market

alongside FCR. Because of the minimum one-hour storage depth requirement in the

Belgian CRM, part of the energy capacity is not used in the FCR and can be used for

intraday market trading. The required capacity remuneration decreases significantly

from 55 to 35 €/kW/year.

This research has shown that both uncertainties about price developments and auction

design have a major impact on the required capacity remuneration. On the other hand,

the uncertainties surrounding the auction design can soon be mitigated if the design is

established by law.

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9.2 Answer to the main research question

What is the competitiveness of electrical energy storage in the future Belgian

capacity market?

The research has been able to determine which factors are related to the competitiveness

of BES, such as the battery’s cycling life, storage costs, reference price, strike price and

the de-rating factors. However, uncertainties have been discovered regarding legislation,

rules and prices. Part of this uncertainty will be eliminated when the CRM rules and

auction parameters will be published. Deploying BES on electricity markets is expected

to become more popular due to the falling CAPEX of BESS. Creative solutions are required

to create stable revenue streams. Investing in BES for operating only in the FCR market

might not be a sustainable business case anymore in the future. Blindly relying on

maintaining current FCR prices is unwise. However, today’s average FCR price must

decrease by more than 75% to reach the expected average intraday market price in 2025.

By combining FCR and intraday market income streams, revenues can be improved as it

leads to a 35% lower capacity remuneration compared to the situation in which BES is

only delopyed in the FCR market. Instead of locking down to one revenue stream, BES

capacity providers should steer between the two revenue streams based on price

developments in these markets.

There are two major developments in the electricity market, which might increase the

average capacity remuneration outside capacity provider’s direct sphere of influence.

First, the planned decommissioning of nuclear plants in Belgium and coal-fired power

plants in Germany requires more new-build generation capacity in Europe. BES and gas-

fired peaking power plants might address the shortcomings in the future, which means

the average capacity remuneration rises. Second, the declining FCR price leads to lower

expected incomes of BES capacity providers. This means BES capacity providers require

a relatively higher capacity remuneration to cover its costs. Both factors could increase

the participation of BES in the capacity market if the capacity is required to achieve the

desired adequacy level.

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There might be a significant gap between the low required capacity remuneration of

near-zero for conventional plants as shown in other European CRMs compared to the

relatively high required capacity remuneration of BES as shown in this research. To

conclude, the results are not convincing regarding BES as a competitive technology in the

upcoming Belgium capacity market.

9.3 Policy recommendations

The insights gained from answering the sub-questions and the main research question

are translated in this section into recommendations for the Belgium CRM. The first

recommendations relate to the CRM auction parameters, more specifically the strike

price and the de-rating factors. Both parameters could have a significant influence on the

competitiveness of BES as discussed earlier. In the minimum FCR price scenario, the

difference in required capacity remuneration in the reference and optimistic scenario

might be a decisive factor for competitive bidding in the CRM auction. As the CRM auction

parameters are still under public consultation, Eneco should discuss the research insights

on the competitiveness of BES with Elia.

The second recommendation concerns the development of the FCR price. Elia’s last

adequacy and flexibility study includes day-ahead market forecasts in Belgium for

different CO2 and technology scenarios. The forecasts include concrete (average) prices

from 2020-2030. However, the ancillary services chapter in the study describes only

price scenarios and does not provide specific prices or percentage changes. Although it

might be difficult to estimate the value of ancillary services in the future, Elia has the most

insight into this market through its knowledge and experience. Public price forecasts by

Elia would give potential capacity suppliers a better picture of their potential income,

which might increase the efficiency of the auctions. Capacity providers will be able to

make a better estimate of what they need in the FCR auction and ultimately also in the

CRM auction. Efficient market outcomes need relevant information for the participants.

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9.4 Recommendations for further research

Several areas for further study have been discovered during the different stages of this

research. The first area is related to the development of the FCR market. As mentioned

in 5.4.3 there is no clear picture of how the FCR prices will develop in the future. The FCR

price has a significant impact on the competitiveness of BES and therefore is a relevant

area of study. However, it should be kept in mind that the FCR price is subject to many

uncertain factors such as the development of renewable energy generation, residential

batteries and further integration of European ancillary markets which is strongly

government policy-driven. Modelling future FCR price scenarios is a very complex subject

and probably interesting enough for a new master thesis. The second area is related to

the feasibility of switching between the intraday market and the FCR market. Deployment

strategies of the BESS could lead to optimal revenue stacking and might increase the

competitiveness of BES. It is interesting to investigate whether a quantitative model can

help to determine the optimal battery deployment in the intraday and FCR market, given

the price developments on both markets.

This research deals with uncertainty regarding BESS CAPEX, CRM auction parameters

and the FCR prices used for the scenarios. This uncertainty can be removed by adjusting

the input data of the research and may require less research compared to the other two

recommendations mentioned above. The accuracy of the results can be improved by

using real price quotes from BESS suppliers since this research assumes estimates at the

moment. The CRM rules and auction parameters will be published after this research so

assumptions have been made based on other European CRMs. Using the final de-rating

factor and strike price might influence the results. Furthermore, the FCR scenarios are

based on the prices from on July 1, 2019, untill September 30, 2019. Further integration

of the European ancillary services market starts from July 2020. Therefore, the price

might drop after July 2020 as discussed earlier and taking the prices over longer horizons

could affect the competitiveness of BES.

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10 Reflection

10.1 Scientific relevance

Scientific research has been conducted on many aspects of this study, such as the

suitability of EES technologies per application, the central buyer mechanism based on

reliability options and the competitiveness of EES in this capacity mechanism. Besides,

the contribution of EES to the security of supply and specific market rules related to its

contribution has been studied.

During the literature review, it became clear that much was already known about EES

technologies and suitable applications in the electricity market. Besides, studies have

already been conducted on the central buyer mechanism based on reliability options in

general and the contribution of EES to the security of supply. The reflection on the Belgian

de-rating factor methodology has shown that the United Kingdom has a more appropriate

methodology for determining the contribution of BES based on EENS instead of LoLE. In

practice, the methodology of the United Kingdom is (financially) less favorable for BES

because it results in a lower capacity remuneration.

The reference price, strike price and the de-rating factor have emerged as important

factors for determining the competitiveness of BES during the investigation into the

Belgian CRM. The results have shown that the de-rating factor has a major impact on the

minimum remuneration that BES capacity providers require. The impact of the strike

price seems to be limited in this research. However, the determination of these

parameters and their impact on the competitiveness are underexposed in the existing

literature. This study constitutes a valuable contribution in the field of the impact of CRM

auction parameters on the competitiveness of BES.

This study has shown that Linny-R is a useful, easy-to-use software tool for determing the

expected income of BES. Linny-R has not been used in the past for this purpose in

scientific research. The added value lies mainly in running easily and fast multiple

experiments. For example, the effect of higher electricity prices, greater storage depth

and an increase number of cycles on the income could easily be calculated in Linny-R.

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Many elements of this study have already been discussed in literature. The scientific

contribution of this research lies in the combination of from what is known from the

literature, new insights into the impact of auction parameters on the competitiveness and

a specific approach for assessing the competitiveness of BES in capacity markets based

on reliability options.

10.2 Societal relevance

This research has been conducted in response to an actual question from Eneco whether

it should participate with BES in the Belgian CRM. The constructed evaluation

competitiveness model can also be used by other capacity providers to assess their

competitiveness. The CRM was introduced to encourage investments in generation

capacity and offers an opportunity for the storage of (sustainable) energy through BES.

The social relevance lies in contributing to the formation of appropriate regulations for

the Belgian CRM to create a level playing field for all capacity providers in the auction.

This research shows that the strike price, de-rating factor and investment thresholds are

important factors that determine the competitiveness of BES. Incorrectly estimated CRM

auction parameters can reduce the participation of BES in capacity auctions, possibly in

favour of existing, polluting gas-fired power plants. Even though the contribution to

adequacy is a lot smaller for storage compared to conventional plants, it does contribute

to making the energy sector more sustainable.

10.3 Link to the EPA programme

The EPA Master’s programme focusses on the grand challenges of the 21st century. Policy,

politics, modelling and simulation are the core themes to approach challenges such as

flood risk, food security, clean energy and cybersecurity (TU Delft, n.d.). Battery storage

is strongly related to the grand challenge and seventh United Nations sustainable

development goal: affordable and clean energy for everyone (United Nations, 2015).

Several EPA methods, such as problem analysis, conceptual and quantitative modelling

have been conducted in this research. Subsequently, policy advice is given to Eneco based

on the model outcomes, which is in line with the qualifications of an EPA thesis.

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12. Appendices

12.1 Full cycle definition

An important performance characteristic for the lifetime of batteries is the cycle life. The

cycle life is defined as the number of (full round) charge and after that discharge cycles

before it no longer meets performance standards (Thirugnanam, Saini, & Kumar, 2012).

It is also closely related to the storage costs because of the more cycles a battery performs,

the shorter the battery’s lifetime, which increases the storage costs. The cycling process

of charging and discharging is shown in figure 14.

Figure 28. Cycling process (Thirugnanam, Saini., & Kumar, 2012).

In this research, the definition of an equivalent full-cycle refers to the full charge, and after

that, the full discharge of a BESS with one hour of storage depth. Batteries with a greater

storage depth require fewer (full) cycles for the same operation in the energy market. For

BESS with greater storage depth, the number of performance cycles should be divided by

the hours of storage depth.

Suppose, a BESS is deployed in the FCR market and is obligated to make 160 cycles per

year. According to the definition in this research, a BESS with 1 hour of storage depth

requires 160 cycles but a BESS with 2 hours of storage depth requires 80 cycles and a

BESS with 4 hours requires 40 cycles etc.

134

12.2 Intraday market

12.2.1 Input prices

The table below shows, as an example, first the 24 hours intraday market prices of the

years 2020-2030. 2018 is the initial year. The prices for 2020, 2023, 2025, 2028 and 2030

has been calculated based on the price scenarios mentioned in 5.2.1.

2018-2020: -20,4%

2020-2023: +13,6%

2023-2025: -2%

2025-2028: -8,2%

2028-2030: +13,3%

The prices of 2021, 2022, 2025, 2026, 2027 and 2029 are approximated through linear

interpolation between the calculated years.

Date 2018 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

1-01-18 0:00 25,00 19,90 20,80 21,70 22,61 22,38 22,15 21,55 20,94 20,34 21,69 23,04

1-01-18 1:00 25,00 19,90 21,25 22,16 22,61 22,38 22,15 21,55 20,94 20,34 21,69 23,04

1-01-18 2:00 -0,74 -0,59 -0,63 -0,65 -0,67 -0,66 -0,65 -0,64 -0,62 -0,59 -0,64 -0,68

1-01-18 3:00 11,81 9,40 10,04 10,47 10,68 10,58 10,47 10,18 9,90 9,61 10,25 10,89

1-01-18 4:00 -5,52 -4,39 -4,69 -4,89 -4,99 -4,94 -4,89 -4,76 -4,62 -4,49 -4,79 -5,09

1-01-18 5:00 -34,76 -27,67 -29,55 -30,81 -31,44 -31,12 -30,81 -29,96 -29,12 -28,28 -30,16 -32,04

1-01-18 6:00 -20,36 -16,20 -17,30 -18,04 -18,41 -18,22 -18,04 -17,55 -17,05 -16,56 -17,66 -18,76

1-01-18 7:00 -22,99 -18,30 -19,55 -20,38 -20,79 -20,58 -20,37 -19,82 -19,26 -18,70 -19,95 -21,19

1-01-18 8:00 -16,14 -12,84 -13,72 -14,30 -14,59 -14,45 -14,30 -13,91 -13,52 -13,13 -14,00 -14,87

1-01-18 9:00 -12,29 -9,78 -10,45 -10,89 -11,11 -11,00 -10,89 -10,59 -10,30 -9,99 -10,66 -11,33

1-01-18 10:00 -8,45 -6,72 -7,18 -7,48 -7,64 -7,56 -7,48 -7,28 -7,08 -6,87 -7,33 -7,78

1-01-18 11:00 -4,60 -3,66 -3,91 -4,08 -4,16 -4,12 -4,08 -3,96 -3,85 -3,74 -3,99 -4,24

1-01-18 12:00 16,45 13,09 13,98 14,57 14,87 14,72 14,57 14,17 13,78 13,38 14,27 15,16

1-01-18 13:00 12,52 9,97 10,64 11,10 11,32 11,21 11,09 10,79 10,49 10,19 10,86 11,54

1-01-18 14:00 15,11 12,03 12,85 13,39 13,67 13,53 13,39 13,03 12,66 12,30 13,11 13,93

1-01-18 15:00 20,68 16,46 17,58 18,33 18,70 18,51 18,33 17,83 17,33 16,82 17,94 19,06

1-01-18 16:00 31,86 25,36 27,08 28,23 28,81 28,52 28,23 27,46 26,69 25,91 27,64 29,36

1-01-18 17:00 51,80 41,24 44,04 45,91 46,84 46,37 45,91 44,65 43,40 42,14 44,94 47,75

1-01-18 18:00 63,08 50,21 53,63 55,91 57,04 56,47 55,90 54,37 52,85 51,32 54,73 58,14

1-01-18 19:00 76,87 61,19 65,35 68,12 69,51 68,81 68,12 66,26 64,39 62,53 66,69 70,85

1-01-18 20:00 71,53 56,94 60,81 63,39 64,68 64,04 63,39 61,66 59,93 58,19 62,06 65,93

1-01-18 21:00 56,91 45,30 48,38 50,44 51,46 50,95 50,43 49,05 47,68 46,30 49,38 52,45

1-01-18 22:00 24,99 19,90 21,25 22,15 22,60 22,37 22,15 21,54 20,94 20,33 21,68 23,04

1-01-18 23:00 25,94 20,65 22,06 22,99 23,46 23,23 22,99 22,36 21,73 21,11 22,51 23,91

135

12.2.2 Income build year 2020

Assuming (maximum) 301 full equivalent cycles per year and 1-hour storage depth.

Year Intraday market income[€/MW/year]

2021 15.957

2022 16.750

2023 16.934

2024 16.685

2025 16.749

2026 16.119

2027 15.768

2028 15.269




2026 17.860

2027 17.358

2028 16.856

2029 18.633

2030 18.780

2031 18.780

2032 18.780

2033 18.780



2026 22.434

2027 21.920

2028 21.376

2029 23.509

2030 23.817

2031 23.817

2032 23.817

2033 23.817

136


Assuming (maximum) 479 full equivalent cycles per year and 1 hour storage depth


2031 17.860

2032 17.860

2033 17.860

2034 17.860

2035 17.860

2036 17.860

2037 17.860

2038 17.860

137

12.3 FCR market

12.3.1 Cycles and energy delivery on FCR market

The table below shows the parameters and values assumed for calculating the minimum

(full equivalent) cycles to meet the FCR requirements.

parameters Value Unit

Nominal frequency 50 [Hz]

Minimum frequency deviation 0,01 [Hz]

Maximum frequency deviation 0,2 [Hz]

time unit 360 [s]

correction factor (1/ time unit) 0,002778 [1/s]

Initial state of charge 3,35 [MWh]

Energy capacity 6,7 [MWh]

Minimum State of Charge 2,5 [MWh]

Maximum State of Charge 4,2 [MWh]

138

A snapshot of the frequency data of 2018 is presented in the table below and shows the

first 5 entries (RTE, 2019).

Date [dd/mm/y hh:mm]

Frequency [Hz]

F deviation [Hz]

Energy regulation [MWh]

State [MWh] Up regulation [MWh]

Down regulation [MWh]

1-1-2018 00:00 50,0252 0,0252 0,000368421 3,35 0 0

1-1-2018 00:00 50,0138 0,0138 0,000201754 3,350368421 0,000368421 0

1-1-2018 00:00 49,9983 -0,0017 0 3,350570175 0,000201754 0

1-1-2018 00:00 49,9942 -0,0058 0 3,350570175 0 0

1-1-2018 00:00 49,9978 -0,0022 0 3,350570175 0 0

The values in the column F deviation, energy regulation, state, up regulation and down

regulation are calculated as follows:

Note: If statements are written in the form: IF(logical test; value if true; value if false) and

t-1 indicates the previous time step if applicable. Besides, the State in the first row is the

initial state.

Date: the date is available per 10 seconds for the year 2018;

Frequency: the measured frequency;

F deviation: 𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 – 𝑁𝑜𝑚𝑖𝑛𝑎𝑙 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 ;

Energy regulation:

𝐼𝐹(|𝐹 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛| > 𝑚𝑖𝑛𝑖𝑚𝑢𝑚 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛;

𝐹 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛

(𝑚𝑎𝑥𝑖𝑚𝑢𝑚 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛−𝑚𝑖𝑛𝑖𝑚𝑢𝑚 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛)∗𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟 ; 0) ;

State:

𝐼𝐹(𝑆𝑡𝑎𝑡𝑒𝑡−1 + 𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑔𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡−1 > 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑆𝑡𝑎𝑡𝑒 𝑜𝑓 𝐶ℎ𝑎𝑟𝑔𝑒;

𝑆𝑡𝑎𝑡𝑒𝑡−1 + 𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑔𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡−1 − 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟;

𝐼𝐹(𝑆𝑡𝑎𝑡𝑒𝑡−1 + 𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑔𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡−1

< 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝑆𝑡𝑎𝑡𝑒 𝑜𝑓 𝐶ℎ𝑎𝑟𝑔𝑒; 𝑆𝑡𝑎𝑡𝑒𝑡−1 + 𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑔𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡−1

+ 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟; 𝑆𝑡𝑎𝑡𝑒 + 𝐸𝑛𝑒𝑟𝑔𝑦 𝑟𝑒𝑔𝑢𝑙𝑎𝑡𝑖𝑜𝑛))

Up regulation:

𝐼𝐹(𝑆𝑡𝑎𝑡𝑒 − 𝑆𝑡𝑎𝑡𝑒𝑡−1 > 0; 𝑆𝑡𝑎𝑡𝑒 − 𝑆𝑡𝑎𝑡𝑒𝑡−1; 0)

Down regulation:

𝐼𝐹(𝑆𝑡𝑎𝑡𝑒 − 𝑆𝑡𝑎𝑡𝑒𝑡−1 < 0; 𝑆𝑡𝑎𝑡𝑒 − 𝑆𝑡𝑎𝑡𝑒𝑡−1; 0)

139

As an example, the state of charge of the BESS is calculated based on the frequency data

of January 2018 and the assumptions above and plotted in the figure below.

The table below shows the required (full equivalent) cycles upwards and downwards per

month and the full (paid) charges in MWh to maintain the minimum state of charge of 2,5

MWh.

Month Upwards cycles Downwards cycles Full (paid) charges in MWh

January 29 -28 1,525321637

February 34 -35 15,30648538

March 43 -41 4,681434211

April 35 -35 1,666209064

May 27 -28 2,383605263

June 25 -25 0,465959064

July 28 -28 3,473038012

August 26 -25 0,08530848

September 28 -29 1,040134503

October 32 -30 1,858913743

November 27 -29 0,295216374

December 28 -28 1,55780117

Total 362,26 -361,72 34,3394269

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

18

92

91

78

57

26

78

53

57

13

44

64

15

35

69

62

49

77

14

25

80

35

38

92

81

98

20

91

07

13

71

16

06

51

24

99

31

33

92

11

42

84

91

51

77

71

60

70

51

69

63

31

78

56

11

87

48

91

96

41

72

05

34

52

14

27

32

23

20

12

32

12

92

41

05

72

49

98

52

58

91

3

Stat

e o

f ch

arge

[M

Wh

]

Time step [1/6 s]

140

12.3.2 Price scenarios


Prices are based on capacity prices since the introduction of the regional FCR auction on

July 1, 2019 till September 30, 2019.

Price scenario FCR income [€/MW/year]

Average FCR price (€ 215,83/MW/day) 78.779

Min. FCR price (€ 140,33/MW/day) 51.220

50% of the average FCR price (€ 106,78/MW/day) 39.390

Competitiveness of Battery Energy Storage in the Future ...

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